NIPS 2017 Accepted Papers

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Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Zhen He (University College London) · Shaobing Gao (Sichuan University) · Liang Xiao (National University of Defense Technology) · David Barber (University College London)

Concentration of Multilinear Functions of the Ising Model with Applications to Network Data
Constantinos Daskalakis (MIT) · Nishanth Dikkala (MIT) · Gautam Kamath (MIT)

Deep Subspace Clustering Network
Pan Ji (University of Adelaide) · Tong Zhang (The Australian National University) · Hongdong Li (Australian National University) · Mathieu Salzmann (EPFL) · Ian Reid (University of Adelaide)

Attentional Pooling for Action Recognition
Rohit Girdhar (Carnegie Mellon University) · Deva Ramanan (Carnegie Mellon University)

On the Consistency of Quick Shift
Heinrich Jiang (Google)

Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
Yu Liu (The Chinese University of Hong Kong) · Hongyang Li (The Chinese University of Hong Kong) · Xiaogang Wang (The Chinese University of Hong Kong)

Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Fabian Pedregosa (UC Berkeley / ETH Zurich) · Rémi Leblond (INRIA) · Simon Lacoste-Julien (INRIA / ENS Paris)

Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis
Jian Zhao (National University of Singapore) · Lin Xiong (Panasonic R&D Center Singapore) · Panasonic Karlekar Jayashree (Panasonic, Singapore) · Jianshu Li (National University of Singapore) · Fang Zhao (National University of Singapore) · Zhecan Wang (Franklin. W. Olin College of Engineering) · Panasonic Sugiri Pranata (Panasonic, Singapore) · Panasonic Shengmei Shen (Panasonic, Singapore) · Jiashi Feng (National University of Singapore)

Dilated Recurrent Neural Networks
Shiyu Chang (IBM T.J. Watson Research Center) · Yang Zhang (IBM T. J. Watson Research) · Wei Han (University of Illinois at Urbana-Champaign) · Mo Yu (Johns Hopkins University) · Xiaoxiao Guo (IBM Research) · Wei Tan (IBM T. J. Watson Research Center) · Xiaodong Cui () · Michael Witbrock (IBM Research, USA) · Mark A Hasegawa-Johnson (University of Illinois) · Thomas Huang (UIUC)

Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
Saurabh Verma (University of Minnesota) · Zhi-Li Zhang (University of Minnesota)

Scalable Generalized Linear Bandits: Online Computation and Hashing
Kwang-Sung Jun (UW-Madison) · Aniruddha Bhargava (University of Wisconsin-Madison) · Robert Nowak (University of Wisconsion-Madison) · Rebecca Willett (University of Wisconsin)

Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
Chris Oates (Newcastle University) · Steven Niederer (Kings College London) · Angela Lee (King's College London) · François-Xavier Briol (University of Warwick) · Mark Girolami (Imperial College London)

Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Peva Blanchard () · El Mahdi El Mhamdi (EPFL) · Rachid Guerraoui () · Julien Stainer ()

Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning
El Mahdi El Mhamdi (EPFL) · Rachid Guerraoui () · Hadrien Hendrikx (EPFL) · Alexandre Maurer (EPFL)

Interactive Submodular Bandit
Lin Chen (Yale University) · Andreas Krause (ETHZ) · Amin Karbasi (Yale)

Scene Physics Acquisition via Visual De-animation
Jiajun Wu (MIT) · Erika Lu (University of Oxford) · Pushmeet Kohli (DeepMind) · Bill Freeman (MIT/Google) · Josh Tenenbaum (MIT)

Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
Zelun Luo (Stanford University) · Yuliang Zou (Virginia Tech) · Judy Hoffman (UC Berkeley) · Li Fei-Fei (Stanford Uniersity)

Decoding with Value Networks for Neural Machine Translation
Di He (Microsoft Research) · Hanqing Lu (Zhejiang University) · Yingce Xia (University of Science and Technology of China) · Tao Qin (Microsoft Research) · Liwei Wang (Peking University) · Tieyan Liu (Microsoft Research)

Parametric Simplex Method for Sparse Learning
Haotian Pang (Princeton University) · Tuo Zhao (Georgia Tech) · Han Liu (Tencent AI Lab) · Robert J Vanderbei (Princeton University)

Group Sparse Additive Machine
Hong Chen (University of Pittsburgh) · Xiaoqian Wang (University of Pittsburgh) · Heng Huang (Computer Science and Engineering University of Texas at Arlington)

Uprooting and Rerooting Higher-order Graphical Models
Adrian Weller (University of Cambridge) · Mark Rowland (University of Cambridge)

The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Krzysztof Choromanski () · Mark Rowland (University of Cambridge) · Adrian Weller (University of Cambridge)

From Parity to Preference: Learning with Cost-effective Notions of Fairness
Muhammad Bilal Zafar (MPI-SWS) · Isabel Valera () · Manuel Rodriguez (MPI SWS) · Krishna Gummadi (Max Planck Institute for Software Systems) · Adrian Weller (University of Cambridge)

Inferring Generative Model Structure with Static Analysis
Paroma Varma (Stanford University) · Bryan He (Stanford University) · Payal Bajaj (Stanford University) · Nishith Khandwala () · Christopher Ré (Stanford)

Structured Embedding Models for Grouped Data
Maja Rudolph (Columbia University) · Francisco Ruiz () · David Blei (Columbia University)

A Linear-Time Kernel Goodness-of-Fit Test
Wittawat Jitkrittum (Gatsby unit, University College London) · Wenkai Xu (Gatsby Unit, UCL) · Zoltan Szabo (Ecole Polytechnique) · Kenji Fukumizu (Institute of Statistical Mathematics) · Arthur Gretton (Gatsby Unit, UCL)

Cortical microcircuits as gated-recurrent neural networks
Rui Costa (University of Oxford) · Ioannis Alexandros Assael (DeepMind) · Brendan Shillingford (University of Oxford) · Nando de Freitas (University of Oxford) · TIm Vogels (University of Oxford)

k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms
Cong Han Lim (University of Wisconsin-Madison) · Stephen Wright (UW-Madison)

A simple model of recognition and recall memory
Nisheeth Srivastava (IIT Kanpur) · Edward Vul (UCSD)

On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Anton Osokin () · Francis Bach (Inria) · Simon Lacoste-Julien (INRIA / ENS Paris)

Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
Jiasen Lu (Georgia Tech) · Anitha Kannan () · Jianwei Yang (Georgia Tech) · Dhruv Batra () · Devi Parikh (Georgia Tech / Facebook AI Research (FAIR))

MaskRNN: Instance Level Video Object Segmentation
Yuan-Ting Hu (UIUC) · Jia-Bin Huang (Virginia Tech) · Alexander Schwing (University of Illinois at Urbana-Champaign)

Gated Recurrent Convolution Neural Network for OCR
Jianfeng Wang (Beijing University of Posts and Telecommunications) · Xiaolin Hu (Tsinghua University)

Towards Accurate Binary Convolutional Neural Network
Wei Pan (DJI) · Xiaofan Lin (DJI) · Cong Zhao (DJI)

Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks
Wei-Sheng Lai (University of California, Merced) · Jia-Bin Huang (Virginia Tech) · Ming-Hsuan Yang (UC Merced)

Learning a Multi-View Stereo Machine
Abhishek Kar (UC Berkeley) · Jitendra Malik () · Christian Häne (UC Berkeley)

Phase Transitions in the Pooled Data Problem
Jonathan Scarlett (EPFL) · Volkan Cevher (EPFL)

Universal Style Transfer via Feature Transforms
Yijun Li (University of California, Merced) · Chen Fang (Adobe Research) · Jimei Yang (Adobe Research) · Zhaowen Wang (Adobe Research) · Xin Lu (Adobe) · Ming-Hsuan Yang (UC Merced)

On the Model Shrinkage Effect of Gamma Process Edge Partition Models
Iku Ohama (Panasonic Corporation) · Issei Sato (The University of Tokyo/RIKEN) · Takuya Kida (Hokkaido University) · Hiroki Arimura (Hokkaido University)

Pose Guided Person Image Generation
Liqian Ma (KU Leuven) · Xu Jia (KU Leuven) · Qianru Sun (MPI Informatics) · Bernt Schiele (Max Planck Institute for Informatics) · Tinne Tuytelaars (KU Leuven) · Luc Van Gool (KU Leuven)

Inference in Graphical Models via Semidefinite Programming Hierarchies
Murat Erdogdu (Microsoft Research) · Yash Deshpande (MIT) · Andrea Montanari (Stanford)

Variable Importance Using Decision Trees
Arash A Amini (UCLA) · Seyed Jalil Kazemitabar (University of California, Los Angeles) · Adam Bloniarz (Google) · Ameet S Talwalkar (UCLA)

Preventing Gradient Explosions in Gated Recurrent Units
Sekitoshi Kanai (NTT) · Yasuhiro Fujiwara (NTT Software Innovation Center) · Sotetsu Iwamura (NTT Software Innovation center)

On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
Simon Du (Carnegie Mellon University) · Yining Wang (Carnegie Mellon University) · Aarti Singh (CMU)

f-GANs in an Information Geometric Nutshell
Richard Nock (Data61, The Australian National University & The University of Sydney) · Zac Cranko (The Australian National University & Data61) · Aditya K Menon (Data61/CSIRO) · Lizhen Qu (Data61) · Robert C Williamson (Australian National University & Data61)

Multimodal Image-to-Image Translation by Enforcing Bi-Cycle Consistency
Jun-Yan Zhu (UC Berkeley) · Richard Zhang (University of California, Berkeley) · Deepak Pathak (UC Berkeley) · Trevor Darrell (UC Berkeley) · Oliver Wang (Adobe Research) · Eli Shechtman () · Alexei Efros (UC Berkeley)

Mixture-Rank Matrix Approximation for Collaborative Filtering
Dongsheng Li (IBM Research - China) · Chao Chen (Tongji University) · Wei Liu (Tencent Technology (Shenzhen) Company Limited) · Tun Lu (Fudan University) · Ning Gu (Fudan University) · Stephen Chu (IBM Research - China)

Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms
An Bian (ETH Zurich) · Joachim M Buhmann (ETH Zurich) · Andreas Krause (ETHZ) · Kfir Levy (ETH)

Learning with Average Top-k Loss
Yanbo Fan (NLPR, CASIA) · Siwei Lyu (SUNY at Albany) · Yiming Ying (State University of New York at Albany) · Baogang Hu (Chinese Academy of Sciences)

Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi (University of Oxford) · Hakan Bilen (University of Oxford) · Andrea Vedaldi (University of Oxford)

Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions
Ryan Tibshirani (Carnegie Mellon University)

Flat2Sphere: Learning Spherical Convolution for Fast Features from 360° Imagery
Yu-Chuan Su (UT Austin) · Kristen Grauman (UT Austin)

3D Shape Reconstruction by Modeling 2.5D Sketch
Jiajun Wu (MIT) · Yifan Wang (ShanghaiTech University) · Tianfan Xue (MIT CSAIL) · Xingyuan Sun (Shanghai Jiao Tong University) · Bill Freeman (MIT/Google) · Josh Tenenbaum (MIT)

Multimodal Learning and Reasoning for Visual Question Answering
Ilija Ilievski (National University of Singapore) · Jiashi Feng (National University of Singapore)

Adversarial Surrogate Losses for Ordinal Regression
Rizal Fathony (University of Illinois at Chicago) · Mohammad Ali Bashiri (University of Illinois at Chicago) · Brian Ziebart (University of Illinois at Chicago)

Hypothesis Transfer Learning via Transformation Functions
Simon Du (Carnegie Mellon University) · Jayanth Koushik (Carnegie Mellon University) · Aarti Singh (CMU) · Barnabas Poczos (Carnegie Mellon University)

Adversarial Invariant Feature Learning
Qizhe Xie (Carnegie Mellon University) · Zihang Dai (CMU) · Yulun Du (Carnegie Mellon University) · Eduard Hovy (CMU) · Graham Neubig (Carnegie Mellon University)

Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Yuanzhi Li (Princeton University) · Yang Yuan (Cornell University)

Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
Tomoya Murata (NTT DATA Mathematical Systems Inc.) · Taiji Suzuki ()

Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
Nanyang Ye (University of Cambridge) · Zhanxing Zhu (Peking University) · Rafal Mantiuk (University of Cambridge)

Efficient Online Linear Optimization with Approximation Algorithms
Dan Garber (Technion - Israel Institute of Technology)

Geometric Descent Method for Convex Composite Minimization
Shixiang Chen (The Chinese University of HongKong) · Shiqian Ma (UC Davis) · Wei Liu (Tencent Technology (Shenzhen) Company Limited)

Diffusion Approximations for Online Principal Component Estimation and Global Convergence
Chris Junchi Li (Princeton University) · Mengdi Wang (Princeton University) · Tong Zhang (Tencent AI Lab)

Avoiding Discrimination through Causal Reasoning
Niki Kilbertus (MPI Tuebingen & Cambridge) · Mateo Rojas Carulla (University of Cambridge, Max Planck for Intelligent Systems) · Giambattista Parascandolo () · Moritz Hardt (UC Berkeley) · Dominik Janzing (MPI Tübingen) · Bernhard Schölkopf (MPI for Intelligent Systems)

Nonparametric Online Regression while Learning the Metric
Ilja Kuzborskij (EPFL) · Nicolò Cesa-Bianchi (Università degli Studi di Milano, Italy)

Recycling for Fairness: Learning with Conditional Distribution Matching Constraints
Novi Quadrianto (University of Sussex and HSE) · Viktoriia Sharmanska (Imperial College London)

Safe and Nested Subgame Solving for Imperfect-Information Games
Noam Brown (Carnegie Mellon University) · Tuomas Sandholm (Carnegie Mellon University)

Unsupervised Image-to-Image Translation Networks
Ming-Yu Liu (NVIDIA) · Thomas Breuel () · Jan Kautz (NVIDIA)

Coded Distributed Computing for Inverse Problems
Yaoqing Yang (Carnegie Mellon University) · Pulkit Grover (CMU) · Soummya Kar (Carnegie Mellon University)

A Screening Rule for l1-Regularized Ising Model Estimation
Zhaobin Kuang (University of Wisconsin, Madison) · Sinong Geng (University of Wisconsin Madison) · David Page (UW-Madison)

Improved Dynamic Regret for Non-degeneracy Functions
Lijun Zhang (Nanjing University (NJU)) · Tianbao Yang (The University of Iowa) · Jinfeng Yi (IBM Thomas J. Watson Research Center) · Rong Jin () · Zhi-Hua Zhou (Nanjing University)

Learning Efficient Object Detection Models with Knowledge Distillation
Guobin Chen (University of Missouri) · Wongun Choi (NEC Laboratories) · Xiang Yu (NEC Laboratories America) · Tony Han (University of Missouri) · Manmohan Chandraker (University of California, San Diego)

One-Sided Unsupervised Domain Mapping
Sagie Benaim (Tel Aviv University) · Lior Wolf (Facebook AI Research)

Deep Mean-Shift Priors for Image Restoration
Siavash Arjomand Bigdeli (Universität Bern) · Matthias Zwicker (University of Maryland, College Park) · Paolo Favaro (University of Bern) · Meiguang Jin (University of Bern)

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Francesco Locatello (MPI - ETH Zürich) · Michael Tschannen (ETH Zurich) · Gunnar Raetsch (ETHZ) · Martin Jaggi (EPFL)

A New Theory for Nonconvex Matrix Completion
Guangcan Liu (Nanjing University of Information Science & Technology (NUIST)) · Xiaotong Yuan () · Qingshan Liu ()

Robust Hypothesis Test for Functional Effect with Gaussian Processes
Jeremiah Liu (Harvard University) · Brent Coull (Harvard University)

Lower bounds on the robustness to adversarial perturbations
Jonathan Peck (Ghent University) · Yvan Saeys (Ghent University) · Bart Goossens (Ghent University) · Joris Roels (Ghent University)

Minimizing a Submodular Function from Samples
Eric Balkanski (Harvard University) · Yaron Singer (Harvard University)

Introspective Classification with Convolutional Nets
Long Jin (University of California San Diego) · Justin Lazarow (UC San Diego) · Zhuowen Tu ()

Label Distribution Learning Forests
Wei Shen (Shanghai University) · KAI ZHAO (Nankai University) · Yilu Guo (Shanghai University) · Alan Yuille (Johns Hopkins University)

Unsupervised object learning from dense equivariant image labelling
James Thewlis (University of Oxford) · Andrea Vedaldi (University of Oxford) · Hakan Bilen (University of Oxford)

Compression-aware Training of Deep Neural Networks
Jose Alvarez (TRI) · Mathieu Salzmann (EPFL)

Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
Daniel Milstein (Brown University) · Jason Pacheco (Brown University) · Brown Leigh Hochberg (Brown, MGH, VA, Harvard) · John D Simeral (Brown University) · Beata Jarosiewicz (Stanford University) · Erik Sudderth (University of California, Irvine)

PredRNN: Recurrent Neural Networks for Video Prediction using Spatiotemporal LSTMs
Yunbo Wang (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University) · Philip S Yu (UIC)

Detrended Partial Cross Correlation for Brain Connectivity Analysis
Jaime Ide (Yale University) · Fábio Cappabianco (Federal University of Sao Paulo) · Fabio Faria (Federal University of Sao Paulo) · Chiang-shan R Li (Yale University)

Contrastive Learning for Image Captioning
Bo Dai (The Chinese University of Hong Kong) · Dahua Lin (The Chinese University of Hong Kong)

Safe Model-based Reinforcement Learning with Stability Guarantees
Felix Berkenkamp (ETH Zurich) · Matteo Turchetta (ETH Zurich) · Angela Schoellig () · Andreas Krause (ETHZ)

Online multiclass boosting
Young Hun Jung (Universith of Michigan) · Jack Goetz (University of Michigan) · Ambuj Tewari (University of Michigan)

Matching on Balanced Nonlinear Representations for Treatment Effects Estimation
Sheng Li (Adobe Research) · Yun Fu (Northeastern University)

Learning Overcomplete HMMs
Vatsal Sharan (Stanford University) · Sham Kakade (University of Washington) · Percy Liang (Stanford University) · Gregory Valiant (Stanford University)

GP CaKe: Effective brain connectivity with causal kernels
Luca Ambrogioni (Donders Institute) · Max Hinne (Radboud University) · Marcel Van Gerven (Radboud University) · Eric Maris (Donders Institute)

Decoupling "when to update" from "how to update"
Eran Malach (Hebrew University Jerusalem Israel) · Shai Shalev-Shwartz (Mobileye & HUJI)

Self-Normalizing Neural Networks
Günter Klambauer (LIT AI Lab / University Linz) · Thomas Unterthiner (LIT AI Lab / University Linz) · Andreas Mayr (LIT AI Lab / University Linz) · Sepp Hochreiter (LIT AI Lab / University Linz)

Learning to Pivot with Adversarial Networks
Gilles Louppe (New York University) · Michael Kagan (SLAC / Stanford) · Kyle Cranmer (New York University)

MolecuLeNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt (TU Berlin) · Pieter-Jan Kindermans (Google Brain Resident) · Huziel E. Sauceda (Fritz-Haber-Institut der Max-Planck-Gesellschaft) · Stefan Chmiela (Technische Universität Berlin) · Alexandre Tkatchenko (University of Luxembourg) · Klaus-Robert Müller (TU Berlin)

Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples
Haw-Shiuan Chang (UMass, Amherst) · Andrew McCallum (UMass Amherst) · Erik Learned-Miller (UMass Amherst)

Differentiable Learning of Submodular Functions
Josip Djolonga (ETH Zurich) · Andreas Krause (ETHZ)

Inductive Representation Learning on Large Graphs
Will Hamilton (Stanford University) · Zhitao Ying (Stanford University) · Jure Leskovec (Stanford University)

Subset Selection for Sequential Data
Ehsan Elhamifar (Northeastern University)

Question Asking as Program Generation
Anselm Rothe (New York University) · Brenden Lake (New York University) · Todd Gureckis (New York University)

Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces
Songbai Yan (University of California, San Diego) · Chicheng Zhang (University of California San Diego)

Gradient Descent Can Take Exponential Time to Escape Saddle Points
Simon Du (Carnegie Mellon University) · Chi Jin (UC Berkeley) · Jason D Lee (USC) · Michael Jordan (UC Berkeley) · Aarti Singh (CMU) · Barnabas Poczos (Carnegie Mellon University)

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
Kristofer Bouchard (Lawrence Berkeley National Laboratory) · Alejandro Bujan (UC Berkeley) · Farbod Roosta-Khorasani (University of California Berkeley) · Shashanka Ubaru (University of Minnesota) · Mr. Prabhat (LBL/NERSC) · Antoine Snijders () · Jian-Hua Mao () · Edward Chang () · Michael W Mahoney (UC Berkeley) · Sharmodeep Bhattacharya ()

One-Shot Imitation Learning
Yan Duan (UC Berkeley) · Marcin Andrychowicz (OpenAI) · Bradly Stadie (OpenAI) · OpenAI Jonathan Ho (OpenAI, UC Berkeley) · Jonas Schneider (OpenAI) · Ilya Sutskever () · Pieter Abbeel (OpenAI / UC Berkeley / Gradescope) · Wojciech Zaremba (OpenAI)

Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Mainak Jas (Télécom ParisTech) · Tom Dupré la Tour (Télécom ParisTech) · Umut Simsekli (Bogazici University) · Alexandre Gramfort (LTCI, CNRS, Télécom ParisTech, Université Paris-Saclay)

Integration Methods and Optimization Algorithms
Damien Scieur (INRIA - ENS) · Vincent Roulet (INRIA / ENS Ulm) · Francis Bach (Inria) · Alexandre d'Aspremont (CNRS - Ecole Normale Supérieure)

Sharpness, Restart and Acceleration
Vincent Roulet (INRIA / ENS Ulm) · Alexandre d'Aspremont (CNRS - Ecole Normale Supérieure)

Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Naoya Takeishi (The University of Tokyo) · Yoshinobu Kawahara (Osaka University) · Takehisa Yairi (The University of Tokyo)

Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Eirikur Agustsson (ETH Zurich) · Fabian Mentzer (ETH Zurich) · Michael Tschannen (ETH Zurich) · Lukas Cavigelli (ETH Zurich) · Radu Timofte (ETH Zurich) · Luca Benini (ETH Zurich) · Luc V Gool (Computer Vision Lab, ETH Zurich)

Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
Stéphanie ALLASSONNIERE (Ecole Polytechnique) · Juliette Chevallier (École polytechnique)

Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications
Qinshi Wang (Princeton University) · Wei Chen (Microsoft Research)

Predictive-State Decoders: Encoding the Future into Recurrent Networks
Arun Venkatraman (Carnegie Mellon University) · Nicholas Rhinehart (Carnegie Mellon University) · Wen Sun (Carnegie Mellon University) · Lerrel Pinto () · Martial Hebert (cmu) · Byron Boots (Georgia Tech / Google Brain) · Kris Kitani (Carnegie Mellon University) · J. Bagnell (Carnegie Mellon University)

Posterior sampling for reinforcement learning: worst-case regret bounds
Shipra Agrawal () · Randy Jia (Columbia University)

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen (The Curious AI Company) · Harri Valpola (The Curious AI Company)

Matching neural paths: transfer from recognition to correspondence search
Nikolay Savinov (ETH Zurich) · Lubor Ladicky (ETH Zurich) · Marc Pollefeys (ETH Zurich)

Linearly constrained Gaussian processes
Carl Jidling (Uppsala University) · Niklas Wahlström (Uppsala University) · Adrian Wills (University of Newcastle, Australia) · Thomas B Schön (Uppsala University)

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Joel A Tropp (Caltech) · Alp Yurtsever (École Polytechnique Fédérale de Lausanne, Switzerland) · Madeleine Udell (Cornell) · Volkan Cevher (EPFL)

Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Karol Hausman (University of Southern California) · Yevgen Chebotar (University of Southern California) · Stefan Schaal (USC) · Gaurav Sukhatme (USC) · Joseph J Lim (University of Southern California)

Learning to Inpaint for Image Compression
Mohammad Haris Baig (Dartmouth College) · Vladlen Koltun (Intel Labs) · Lorenzo Torresani (Dartmouth)

Adaptive Bayesian Sampling with Monte Carlo EM
Anirban Roychowdhury (Ohio State University) · Srinivasan Parthasarathy (The Ohio State University)

No More Fixed Penalty Parameter in ADMM: Faster Convergence with New Adaptive Penalization
Yi Xu (The University of Iowa) · Mingrui Liu (The University of Iowa) · Tianbao Yang (The University of Iowa) · Univ Qihang Lin (Univ Iowa, faculty)

Shape and Material from Sound
zhoutong zhang (MIT) · Qiujia Li (University of Cambridge) · Zhengjia Huang () · Jiajun Wu (MIT) · Josh Tenenbaum (MIT) · Bill Freeman (MIT/Google)

Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann (research center caesar, an associate of the Max Planck Society) · Pedro J Goncalves (research center caesar, an associate of the Max Planck Society) · Giacomo Bassetto (research center caesar) · Kaan Oecal (research center caesar) · Marcel Nonnenmacher (research center caesar, an associate of the Max Planck Society) · Jakob H Macke (research center caesar, an associate of the Max Planck Society)

Online Prediction with Selfish Experts
Tim Roughgarden (Stanford University) · Okke Schrijvers (Facebook Inc.)

Tensor Biclustering
Soheil Feizi (Stanford University) · Hamid Javadi (Stanford University) · David Tse (Stanford University)

DPSCREEN: Dynamic Personalized Screening
Kartik Ahuja (University of California, Los Angeles) · William Zame (UCLA) · Mihaela van der Schaar (UCLA and Oxford University)

Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Yi Ouyang (University of California, Berkeley) · Mukul Gagrani (University of Southern California) · Ashutosh Nayyar (University of Southern California) · Rahul Jain (University of Southern California)

Testing and Learning on Distributions with Symmetric Noise Invariance
Law Ho Chung (University Of Oxford) · Christopher Yau (University of Oxford) · Dino Sejdinovic (University of Oxford)

A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
Hongteng Xu (Duke University) · Hongyuan Zha (Georgia Tech)

Deanonymization in the Bitcoin P2P Network
Giulia Fanti (Carnegie Mellon University) · Pramod Viswanath (UIUC)

Accelerated consensus via Min-Sum Splitting
Patrick Rebeschini (University of Oxford) · Sekhar C Tatikonda (Yale University)

Generalized Linear Model Regression under Distance-to-set Penalties
Jason Xu (NSF Postdoctoral Fellow UCLA) · Eric Chi (North Carolina State University) · Kenneth Lange (UCLA)

Adaptive sampling for a population of neurons
Benjamin Cowley (Carnegie Mellon University) · Ryan Williamson (Carnegie Mellon University) · Katerina Clemens (University of Pittsburgh) · Matthew Smith (University of Pittsburgh) · Byron M Yu (Carnegie Mellon University)

Nonbacktracking Bounds on the Influence in Independent Cascade Models
Emmanuel Abbe (Princeton University) · Sanjeev Kulkarni (Princeton University) · Eun Jee Lee (Princeton University)

Learning with Feature Evolvable Streams
Bo-Jian Hou (LAMDA Group) · Lijun Zhang (Nanjing University (NJU)) · Zhi-Hua Zhou (Nanjing University)

Online Convex Optimization with Stochastic Constraints
Hao Yu (University of Southern California) · Michael Neely (Univ. Southern California) · Xiaohan Wei (University of Southern California)

Max-Margin Invariant Features from Transformed Unlabelled Data
Dipan Pal (Carnegie Mellon University) · Ashwin Kannan (Carnegie Mellon University) · Gautam Arakalgud (Carnegie Mellon University) · Marios Savvides (Carnegie Mellon University)

Cognitive Impairment Prediction in Alzheimer’s Disease with Regularized Modal Regression
Xiaoqian Wang (University of Pittsburgh) · Hong Chen (University of Pittsburgh) · Dinggang Shen (UNC-Chapel Hill) · Heng Huang (Computer Science and Engineering University of Texas at Arlington)

Translation Synchronization via Truncated Least Squares
Xiangru Huang (UT Austin) · Zhenxiao Liang (Tsinghua University) · Chandrajit Bajaj (The University of Texas at Austin) · Qixing Huang (The University of Texas at Austin)

From which world is your graph
Cheng Li (College of William and Mary) · Varun Kanade (University of Oxford) · Felix MF Wong (Google) · Zhenming Liu (William and Mary)

A New Alternating Direction Method for Linear Programming
Sinong Wang (The Ohio State University) · Ness Shroff (The Ohio State University)

Regret Analysis for Continuous Dueling Bandit
Wataru Kumagai (Kanagawa University)

Best Response Regression
Omer Ben Porat (Technion – Israel Institute of Technology) · Moshe Tennenholtz (Technion--Israel Institute of Technology)

TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Wei Wen (Duke University) · Cong Xu (Hewlett Packard Labs) · Feng Yan (University of Nevada, Reno) · Chunpeng Wu (Duke University) · Yandan Wang (University of Pittsburgh) · Yiran Chen (Duke University) · Hai Li (Duke University)

Learning Affinity via Spatial Propagation Networks
Sifei Liu (Nvidia) · Guangyu Zhong (Dalian University of Technology) · Ming-Hsuan Yang (UC Merced) · Shalini De Mello (NVIDIA) · Jan Kautz (NVIDIA) · Jinwei Gu (NVIDIA Research)

Linear regression without correspondence
Daniel Hsu () · Kevin Shi (Columbia University) · Xiaorui Sun (Columbia University)

NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
Martin J Zhang (Stanford University) · Fei Xia (Stanford University) · James Zou (Stanford) · David Tse (Stanford University)

Cost efficient gradient boosting
Sven Peter (University Heidelberg) · Ferran Diego () · Fred Hamprecht (Heidelberg University) · Boaz Nadler (Weizmann Institute of Science)

Probabilistic Rule Realization and Selection
Haizi Yu (University of Illinois at Urbana-Champaign) · Tianxi Li (University of Michigan) · Lav Varshney (University of Illinois at Urbana-Champaign)

Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
Aryeh Kontorovich (Ben Gurion University) · Sivan Sabato (Ben Gurion University) · Roi Weiss (Weizmann institute of science)

A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Tor Lattimore (DeepMind)

Learning Multiple Tasks with Deep Relationship Networks
Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University) · Philip S Yu (UIC)

Deep Hyperalignment
Muhammad Yousefnezhad (Nanjing University of Aeronautics and Astronautics) · Daoqiang Zhang (Nanjing University of Aeronautics and Astronautics)

Online to Offline Conversions and Adaptive Minibatch Sizes
Kfir Levy (ETH)

Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure
Alberto Bietti (Inria) · Julien Mairal (Inria)

Deep Learning with Topological Signatures
Christoph Hofer (University of Salzburg) · Roland Kwitt (University of Salzburg) · Marc Niethammer (UNC Chapel Hill) · Andreas Uhl (University of Salzburg)

Predicting User Activity Level In Point Process Models With Mass Transport Equation
Yichen Wang (Georgia Tech) · Xiaojing Ye (Georgia State University) · Hongyuan Zha (Georgia Tech) · Le Song (Georgia Institute of Technology)

Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues
Noga Alon (Tel Aviv University) · Moshe Babaioff (Microsoft Research) · Yannai A. Gonczarowski (The Hebrew University of Jerusalem and Microsoft Research) · Yishay Mansour (Tel Aviv University) · Shay Moran (IAS, Princeton) · Amir Yehudayoff (Technion - Israel institue of Technology)

Deep Dynamic Poisson Factorization Model
Chengyue Gong (PeKing University) · win-bin huang (peking university)

Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo (UTokyo/RIKEN) · Gang Niu (The University of Tokyo) · Marthinus C du Plessis (The University of Tokyo) · Masashi Sugiyama (RIKEN / University of Tokyo)

Optimal Sample Complexity of M-wise Data for Top-K Ranking
Minje Jang (KAIST) · Sunghyun Kim (ETRI) · Changho Suh (KAIST) · Sewoong Oh (UIUC)

What-If Reasoning using Counterfactual Gaussian Processes
Peter Schulam (Johns Hopkins University) · Suchi Saria (Johns Hopkins University)

Communication-Efficient Stochastic Gradient Descent, with Applications to Neural Networks
Dan Alistarh (IST Austria & ETH Zurich) · Demjan Grubic (ETH Zurich) · Jerry Li (MIT) · Ryota Tomioka (Microsoft Research Cambridge) · Milan Vojnovic (London School of Economics and Political Science (LSE))

On the Convergence of Block Coordinate Descent in Training DNNs with Tikhonov Regularization
Ziming Zhang (MERL) · Matthew Brand (Mitsubishi Electric Research Labs)

Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer (Technion) · Itay Hubara (Technion) · Daniel Soudry (Technion)

Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Arjun K Bansal (Intel Nervana) · William Constable (Intel) · Oguz Elibol (Intel) · Stewart Hall (Intel) · Luke Hornof (Intel) · Amir Khosrowshahi (Intel) · Carey Kloss (Intel) · Urs Köster (Intel Corporation) · Marcel Nassar (Intel Corporation) · Naveen Rao (Intel) · Xin Wang (Intel Corporation) · Tristan Webb (Intel / Nervana)

Model evidence from nonequilibrium simulations
Michael Habeck (Max Planck Institute Goettingen)

Minimal Exploration in Structured Stochastic Bandits
Stefan Magureanu (KTH) · Richard Combes (Centrale-Supelec) · Alexandre Proutiere (KTH)

Learned D-AMP: Principled Neural-network-based Compressive Image Recovery
Chris Metzler (Rice University) · Ali Mousavi (Rice University) · Richard Baraniuk (Rice University)

Deliberation Networks: Sequence Generation Beyond One-Pass Decoding
Yingce Xia (University of Science and Technology of China) · Lijun Wu (Sun Yat-sen University) · Jianxin Lin (USTC) · Fei Tian (Miicrosoft Research) · Tao Qin (Microsoft Research) · Tie-Yan Liu (Microsoft Research)

Adaptive Clustering through Semidefinite Programming
Martin Royer (Université Paris-Saclay)

Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning
Liangpeng Zhang (University of Birmingham) · Ke Tang (University of Science and Technology of China) · Xin Yao (University of Birmingham)

Repeated Inverse Reinforcement Learning
Kareem Amin (Google Research) · Nan Jiang (Microsoft Research) · Satinder Singh (University of Michigan)

The Numerics of GANs
Lars Mescheder (Max-Planck Institute Tuebingen) · Sebastian Nowozin (Microsoft Research Cambridge) · Andreas Geiger (MPI Tübingen)

Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Luigi Acerbi (New York University) · Wei Ji (New York University)

Learning Chordal Markov Networks via Branch and Bound
Kari Rantanen (University of Helsinki) · Antti Hyttinen (University of Helsinki) · Matti Järvisalo (University of Helsinki)

Revenue Optimization with Approximate Bid Predictions
Andres Munoz () · Sergei Vassilvitskii (Google)

Solving (Almost) all Systems of Random Quadratic Equations
Gang Wang (University of Minnesota) · Georgios Giannakis (University of Minnesota) · Yousef Saad (University of Minnesota) · Jie Chen (Beijing Institute of Technology)

Unsupervised Learning of Disentangled Latent Representations from Sequential Data
Wei-Ning Hsu (Massachusetts Institute of Technology) · Yu Zhang (Google Brain) · James Glass (MIT CSAIL)

Lookahead Bayesian Optimization with Inequality Constraints
Remi Lam (MIT) · Karen Willcox (MIT)

Hierarchical Methods of Moments
Matteo Ruffini (UPC) · Guillaume Rabusseau (McGill University) · Borja Balle ()

Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts
Raymond Yeh (University of Illinois at Urbana–Champaign) · Jinjun Xiong (IBM Research) · Wen-Mei Hwu () · Minh Do (University of Illinois) · Alexander Schwing (University of Illinois at Urbana-Champaign)

Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network
Lixin Fan (Nokia Technologies)

Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization
Pan Xu (University of Virginia) · Jian Ma (Carnegie Mellon University) · Quanquan Gu (University of Virginia)

Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Sergey Ioffe (Google)

Generating steganographic images via adversarial training
Jamie Hayes (University College London) · George Danezis (University College London)

Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Jason Altschuler (MIT) · Jonathan Weed (MIT) · Philippe Rigollet (MIT)

PixelGAN Autoencoders
Alireza Makhzani (University of Toronto) · Brendan J Frey (Deep Genomics, Vector Institute, Univ. Toronto)

Consistent Multitask Learning with Nonlinear Output Relations
Carlo Ciliberto (University College London) · Alessandro Rudi (University of Genova) · Lorenzo Rosasco (University of Genova- MIT - IIT) · Massimiliano Pontil (University College London & Italian Institute of Technology)

Fast Alternating Minimization Algorithms for Dictionary Learning
Niladri Chatterji (UC Berkeley) · Peter Bartlett (UC Berkeley)

Learning ReLUs via Gradient Descent
Mahdi Soltanolkotabi (University of Southern california)

Stabilizing Training of Generative Adversarial Networks through Regularization
Kevin Roth (ETH) · Aurelien Lucchi (ETH Zurich) · Sebastian Nowozin (Microsoft Research Cambridge) · Thomas Hofmann (ETH Zurich)

Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems
Wen Dong (University at Buffalo) · Le Fang (University at Buffalo-SUNY) · Fan Yang (University at Buffalo) · Tong Guan () · Chunming Qiao ()

Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs
Rowan McAllister (University of Cambridge) · Carl Edward Rasmussen (University of Cambridge)

Compatible Reward Inverse Reinforcement Learning
Alberto Maria Metelli (Politecnico di Milano) · Matteo Pirotta (INRIA Lille-Nord Europe) · Marcello Restelli ()

First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization
Aryan Mokhtari (University of Pennsylvania) · Alejandro Ribeiro (University of Pennsylvania)

Hiding Images in Plain Sight: Deep Steganography
Google Shumeet Baluja (Google, Inc.)

Neural Program Meta-Induction
Jacob Devlin (Microsoft Research) · Rudy R Bunel (Oxford University) · Rishabh Singh (Microsoft Research) · Matthew Hausknecht (Microsoft Research) · Pushmeet Kohli (DeepMind)

Bayesian Dyadic Trees and Histograms for Regression
Stéphanie van der Pas (Leiden University) · Veronika Rockova (University of Chicago)

A graph-theoretic approach to multitasking
Noga Alon (Tel Aviv University) · Daniel Reichman (University of California, Berkeley) · Igor Shinkar (UC Berkeley) · Tal Wagner (MIT) · Sebastian Musslick () · Tom Griffiths (UC Berkeley) · Jonathan D Cohen (Princeton University) · Biswadip dey (Princeton University) · Kayhan Ozcimder (Princeton University)

Consistent Robust Regression
Kush Bhatia (UC Berkeley) · Prateek Jain (Microsoft Research) · Purushottam Kar (Indian Institute of Technology Kanpur)

Natural value approximators: learning when to trust past estimates
Tom Schaul (DeepMind) · Zhongwen Xu (DeepMind) · Joseph Modayil (Deepmind) · Hado van Hasselt (DeepMind) · Andre Barreto (DeepMind) · David Silver (DeepMind)

Bandits Dueling on Partially Ordered Sets
CMLA Julien Audiffren (CMLA, ENS CACHAN) · Liva Ralaivola (LIF, Aix-Marseille University, CNRS)

Elementary Symmetric Polynomials for Optimal Experimental Design
Zelda E. Mariet (MIT) · Suvrit Sra (MIT)

Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Serhii Havrylov (University of Amsterdam) · Ivan Titov ()

Backprop without Learning Rates Through Coin Betting
Francesco Orabona (Stony Brook University) · Tatiana Tommasi (University of Rome La Sapienza)

Pixels to Graphs by Associative Embedding
Alejandro Newell (University of Michigan) · Jia Deng (University of Michigan)

Runtime Neural Pruning
Ji Lin (Tsinghua University) · Yongming Rao (Tsinghua University) · Jiwen Lu (Tsinghua University)

Compressing the Gram Matrix for Learning Neural Networks in Polynomial Time
Surbhi Goel (University of Texas at Austin) · Adam Klivans (UT Austin)

MMD GAN: Towards Deeper Understanding of Moment Matching Network
Chun-Liang Li (Carnegie Mellon University) · Wei-Cheng Chang (Carnegie Mellon University) · Yu Cheng (AI Foundations, IBM Research) · Yiming Yang (CMU) · Barnabas Poczos (Carnegie Mellon University)

The Reversible Residual Network: Backpropagation Without Storing Activations
Aidan N Gomez (University of Toronto) · Mengye Ren (University of Toronto) · Raquel Urtasun (University of Toronto) · Roger Grosse (University of Toronto)

Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
Quentin Berthet (University of Cambridge) · Vianney Perchet (ENS Paris-Saclay & Criteo Research)

Zap Q-Learning
Adithya M Devraj (University of Florida) · Sean P Meyn (University of Florida)

Expectation Propagation for t-Exponential Family Using Q-Algebra
Futoshi Futami (University of Tokyo/RIKEN) · Issei Sato (The University of Tokyo/RIKEN) · Masashi Sugiyama (RIKEN / University of Tokyo)

Few-Shot Learning Through an Information Retrieval Lens
Eleni Triantafillou (University of Toronto) · Richard Zemel (University of Toronto) · Raquel Urtasun (University of Toronto)

Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Matthias Hein (Saarland University) · Maksym Andriushchenko (Saarland University)

Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Alejandro Newell (University of Michigan) · Zhiao Huang (IIIS, Tsinghua University) · Jia Deng (University of Michigan)

Practical Locally Private Heavy Hitters
kobbi nissim (Georgetown University) · Raef Bassily (UCSD) · Uri Stemmer (Harvard University) · Abhradeep Thakurta (APPLE Inc)

Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
Kinjal Basu (LinkedIn) · Ankan Saha (University of Chicago) · Shaunak Chatterjee ()

Inhomogoenous Hypergraph Clustering with Applications
Pan Li (University of Illinois Urbana-Champaign) · Olgica Milenkovic (University of Illinois at Urbana-Champaign)

Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Fan Yang (Carnegie Mellon University) · Zhilin Yang (Carnegie Mellon University) · William W Cohen (Carnegie Mellon University)

Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks
Ahmed Ibrahim (UCLA) · Mihaela van der Schaar (UCLA and Oxford University)

Masked Autoregressive Flow for Density Estimation
George Papamakarios (University of Edinburgh) · Iain Murray (University of Edinburgh) · Theo Pavlakou (The University of Edinburgh)

Non-convex Finite-Sum Optimization Via SCSG Methods
Lihua Lei (UC Berkeley) · Cheng Ju (University of California, Berkeley) · Jianbo Chen (University of California, Berkeley) · Michael Jordan (UC Berkeley)

Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
Rebecca Morrison (Massachusetts Institute of Technology) · Ricardo Baptista (MIT) · Youssef Marzouk (Massachusetts Institute of Technology)

Inner-loop free ADMM using Auxiliary Deep Neural Networks
Kai Fan (Duke University) · Qi Wei (Duke University) · Katherine A Heller (Duke)

OnACID: Online Analysis of Calcium Imaging Data in Real Time
Andrea Giovannucci (Flatiron Institute, Simons Foundation) · Johannes Friedrich (Columbia University) · Matt Kaufman (Cold Spring Harbor Laboratory) · Anne Churchland (Cold Spring Harbor Laboratory) · Dmitri Chklovskii (Simons Foundation) · Liam Paninski (Columbia University) · Eftychios Pnevmatikakis (Flatiron Institute)

Collaborative PAC Learning
Avrim Blum (CMU) · Nika Haghtalab (Carnegie Mellon University) · Ariel D Procaccia (Carnegie Mellon University) · IIIS Mingda Qiao (IIIS, Tsinghua University)

Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
Jeffrey Regier (UC Berkeley) · Michael Jordan (UC Berkeley) · Jon McAuliffe (UC Berkeley)

Scalable Demand-Aware Recommendation
Jinfeng Yi (IBM Thomas J. Watson Research Center) · Cho-Jui Hsieh (UC Davis) · Kush R Varshney (IBM Research) · Lijun Zhang (Nanjing University (NJU)) · Yao Li (University of California, Davis)

SGD Learns the Conjugate Kernel Class of the Network
Amit Daniely (Google Research)

Noise-Tolerant Interactive Learning Using Pairwise Comparisons
Yichong Xu (Carnegie Mellon University) · Hongyang Zhang (Carnegie Mellon University) · Aarti Singh (Carnegie Mellon University) · Artur Dubrawski (Carnegie Mellon University) · Kyle Miller (Carnegie Mellon University)

Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Yonatan Belinkov (MIT)

Generative Local Metric Learning for Kernel Regression
Yung-Kyun Noh (Seoul National University) · Masashi Sugiyama (RIKEN / University of Tokyo) · Kee-Eung Kim (KAIST) · Frank Park (Seoul National University) · Daniel Lee (University of Pennsylvania)

Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Linus Hamilton (MIT) · Frederic Koehler (MIT) · Ankur Moitra ()

Fitting Low-Rank Tensors in Constant Time
Kohei Hayashi (AIST / RIKEN) · Yuichi Yoshida (National Institute of Informatics and Preferred Infrastructure, Inc.)

Deep supervised discrete hashing
Qi Li (Institute of Automation, Chinese Academy of Sciences) · Zhenan Sun (Institute of Automation, Chinese Academy of Sciences (CASIA)) · Ran He (CASIA) · Tieniu Tan (Chinese Academy of Sciences)

Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
Zhaohan Guo (Carnegie Mellon University/Stanford) · Philip S. Thomas (CMU) · Emma Brunskill (CMU)

How regularization affects the critical points in linear networks
Amirhossein Taghvaei (University of Illinois at Urbana-Champaign) · Jin W Kim (University of Illinois) · Prashant Mehta (University of Illinois)

Fisher GAN
Youssef Mroueh (IBM T.J Watson Research Center) · Tom Sercu (IBM Research)

Information-theoretic analysis of generalization capability of learning algorithms
Maxim Raginsky (University of Illinois at Urbana-Champaign) · Aolin Xu (University of Illinois at Urbana-Champaign)

Sparse Approximate Conic Hulls
Greg Van Buskirk (UT Dallas) · Ben Raichel (UT Dallas) · Nicholas Ruozzi (UTDallas)

Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
Alyson Fletcher (UCLA, UCSC, & UC Berkeley) · Sundeep Rangan (NYU-Poly) · Mojtaba Sahraee-Ardakan (UCLA) · Philip Schniter (Ohio State University)

Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
Chengxu Zhuang (Stanford University) · Jonas Kubilius (Massachusetts Institute of Technology) · Mitra JZ Hartmann (Northwestern University) · Daniel Yamins (Stanford University)

Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
Steven Wu (Microsoft Research) · Bo Waggoner () · Seth Neel (University of Pennsylvania) · Aaron Roth (University of Pennsylvania) · Katrina Ligett ()

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Justin Fu (UC Berkeley) · John Co-Reyes (UC Berkeley) · Sergey Levine (UC Berkeley)

Multitask Spectral Learning of Weighted Automata
Guillaume Rabusseau (McGill University) · Borja Balle () · Joelle Pineau (McGill University)

Multi-way Interacting Regression via Factorization Machines
Mikhail Yurochkin (University of Michigan) · XuanLong Nguyen (University of Michigan) · nikolaos Vasiloglou (LogicBlox)

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Wengong Jin (MIT CSAIL) · Connor W Coley (MIT Department of Chemical Engineering) · Regina Barzilay (Massachusetts Institute of Technology) · Tommi Jaakkola (MIT)

Practical Data-Dependent Metric Compression with Provable Guarantees
Piotr Indyk (MIT) · Ilya Razenshteyn (Columbia University) · Tal Wagner (MIT)

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker (Google Brain) · Andriy Mnih () · Chris J Maddison (University of Oxford / DeepMind) · John Lawson (Google Brain) · Jascha Sohl-Dickstein (Google Brain)

Nonlinear random matrix theory for deep learning
Jeffrey Pennington (Google Brain) · Pratik Worah (Google)

Parallel Streaming Wasserstein Barycenters
Matthew Staib (MIT) · Sebastian Claici (MIT) · Justin M Solomon (MIT) · Stefanie Jegelka (MIT)

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
Yuandong Tian (Facebook AI Research) · Qucheng Gong (Facebook AI Research) · Wenling Shang (University of Amsterdam) · Yuxin Wu (Facebook AI Research) · C. Lawrence Zitnick (Facebook AI Research)

Dual Discriminator Generative Adversarial Nets
Tu Nguyen (Deakin University) · Trung Le (Deakin University) · Hung Vu (Deakin University) · Dinh Phung (Deakin University)

Dynamic Revenue Sharing
Santiago Balseiro (Duke University) · Max Lin (Google) · Vahab Mirrokni (Google Research NYC) · Renato Leme (Google Research) · IIIS Song Zuo (IIIS, Tsinghua University)

Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search
Mohammad Ali Bashiri (University of Illinois at Chicago) · Xinhua Zhang (University of Illinois at Chicago)

Multi-agent Predictive Modeling with Attentional CommNets
Yedid Hoshen (Facebook AI Research)

An Empirical Bayes Approach to Optimizing Machine Learning Algorithms
James McInerney (Spotify Research)

Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Di Wang (State University of New York at Buffalo) · Minwei Ye (University at Buffalo) · Jinhui Xu (SUNY at Buffalo)

Variational Inference via $\chi$ Upper Bound Minimization
Adji Bousso Dieng (Columbia University) · Dustin Tran (Columbia University & OpenAI) · Rajesh Ranganath (Princeton University) · John Paisley () · David Blei (Columbia University)

On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
Xingguo Li (University of Minnesota) · Lin Yang () · Jason Ge (Princeton University) · Jarvis Haupt (University of Minnesota) · Tong Zhang (Tencent AI Lab) · Tuo Zhao (Georgia Tech)

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang (UC Berkeley) · Pieter Abbeel (OpenAI / UC Berkeley / Gradescope) · Davis Foote (Google Brain) · Yan Duan () · OpenAI Xi Chen (OpenAI, UC Berkeley) · Rein Houthooft (OpenAI) · Adam Stooke (UC Berkeley) · Filip DeTurck ()

An Empirical Study on The Properties of Random Bases for Kernel Methods
Maximilian Alber (TU Berlin) · Pieter-Jan Kindermans (Google Brain Resident) · Kristof Schütt (TU Berlin) · Klaus-Robert Müller (TU Berlin) · Fei Sha (University of Southern California (USC))

Bridging the Gap Between Value and Policy Based Reinforcement Learning
Ofir Nachum (Google) · Mohammad Norouzi () · Kelvin Xu (Google) · Dale Schuurmans (Google)

Premise Selection for Theorem Proving by Deep Graph Embedding
Mingzhe Wang (University of Michigan) · Yihe Tang (University of Michigan) · Jian Wang (University of Michigan) · Jia Deng (University of Michigan)

A Bayesian Data Augmentation Approach for Learning Deep Models
Toan M Tran (The University of Adelaide) · Trung T Pham (The University of Adelaide) · Gustavo Carneiro (The University of Adelaide) · Lyle Palmer (The University of Adelaide) · Ian Reid (University of Adelaide)

Principles of Riemannian Geometry in Neural Networks
Michael Hauser (Pennsylvania State University) · Asok Ray (Pennsylvania State University)

Cold-Start Reinforcement Learning with Softmax Policy Gradients
Nan Ding (Google) · Radu Soricut (Google)

Online Dynamic Programming
Holakou Rahmanian (University of California at Santa Cruz) · Manfred K Warmuth (Univ. of Calif. at Santa Cruz)

Alternating Estimation for Structured High-Dimensional Multi-Response Models
Sheng Chen (University of Minnesota) · Arindam Banerjee (University of Minnesota)

Convolutional Gaussian Processes
Mark van der Wilk (University of Cambridge) · Carl Edward Rasmussen (University of Cambridge) · James Hensman (PROWLER.io)

Estimation of the covariance structure of heavy-tailed distributions
Xiaohan Wei (University of Southern California) · Stanislav Minsker (USC)

Mean Field Residual Networks: On the Edge of Chaos
Ge Yang (Harvard University)

Decomposable Submodular Function Minimization: Discrete and Continuous
Alina Ene (University of Warwick) · Huy Nguyen (Northeastern University) · Laszlo Vegh (London School of Economics)

Gauging Variational Inference
Sung-Soo Ahn (KAIST) · Michael Chertkov (Los Alamos National Laboratory) · Jinwoo Shin (KAIST)

Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
Seunghyun Park (Seoul National University) · Seonwoo Min (Seoul National University) · Hyun-soo Choi (Seoul Nation University) · Sungroh Yoon (Seoul National University)

Robust Estimation of Neural Signals in Calcium Imaging
Hakan Inan (Stanford University) · Murat Erdogdu (Microsoft Research) · Mark Schnitzer (Stanford University)

State Aware Imitation Learning
Yannick Schroecker (Georgia Institute of Technology) · Charles L Isbell (Georgia Tech)

Beyond Parity: Fairness Objectives for Collaborative Filtering
Sirui Yao (Virginia Polytechnic Institute and State University) · Bert Huang (Virginia Tech)

A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
Ben London (Amazon)

Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
Roel Dobbe (UC Berkeley) · David Fridovich-Keil (UC Berkeley) · Claire Tomlin (UC Berkeley)

Model-Powered Conditional Independence Test
Rajat Sen (University of Texas at Austin) · Ananda Theertha Suresh (Google) · Karthikeyan Shanmugam (IBM Research, NY) · Alexandros Dimakis (University of Texas, Austin) · Sanjay Shakkottai (The University of Texas at Austin)

Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Andrew Gibiansky (Baidu Research)

Variance-based Regularization with Convex Objectives
Hongseok Namkoong (Stanford University) · John C Duchi (Stanford)

Deep Lattice Networks and Partial Monotonic Functions
Seungil You (Google) · David Ding (Google) · Kevin Canini (Google) · Jan Pfeifer (Google) · Maya Gupta (Google)

Continual Learning with Deep Generative Replay
Hanul Shin (Massachusetts Institute of Technology) · Jung Kwon Lee (SK T-Brain) · Jaehong Kim (T-Brain) · Jiwon Kim (SK T-Brain)

AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Marco Cusumano-Towner (Massachusetts Institute of Technology) · Vikash K Mansinghka (Massachusetts Institute of Technology)

Learning Causal Structures Using Regression Invariance
AmirEmad Ghassami (University of Illinois at Urbana–Champaign) · Saber Salehkaleybar (University of Illinois at Urbana-Champaign) · Negar Kiyavash (UIUC) · Kun Zhang (CMU)

Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
Zheng Wen (Adobe Research) · Branislav Kveton (Adobe Research) · Michal Valko (Inria Lille - Nord Europe) · Sharan Vaswani (University of British Columbia)

Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem
Yasin Abbasi (Adobe Research) · Peter Bartlett (UC Berkeley) · Victor Gabillon (QUT - ACEMS)

Reinforcement Learning under Model Mismatch
Aurko Roy (Google Brain) · Huan Xu () · Sebastian Pokutta (Georgia Institute of Technology)

Hierarchical Attentive Recurrent Tracking
Adam Kosiorek (University of Oxford) · Alex Bewley (University of Oxford) · Ingmar Posner (Oxford University)

Tomography of the London Underground: a Scalable Model for Origin-Destination Data
Nicolò Colombo (University College London) · Ricardo Silva (University College London) · Soong Moon Kang (University College London)

Rotting Bandits
Nir Levine (Technion - Israel Institute of Technology) · Koby Crammer (Technion) · Shie Mannor (Technion)

Unbiased estimates for linear regression via volume sampling
Michal Derezinski (UC Santa Cruz) · Manfred K Warmuth (Univ. of Calif. at Santa Cruz)

An Applied Algorithmic Foundation for Hierarchical Clustering
Joshua Wang (Stanford University) · Benjamin Moseley (Washington University in St Lo)

Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition
Mingrui Liu (The University of Iowa) · Tianbao Yang (The University of Iowa)

Stein Variational Gradient Descent as Gradient Flow
Qiang Liu (Dartmouth College)

Partial Hard Thresholding: A Towards Unified Analysis of Support Recovery
Jie Shen (Rutgers University) · Ping Li (Rugters University)

Shallow Updates for Deep Reinforcement Learning
Nir Levine (Technion - Israel Institute of Technology) · Tom Zahavy (The Technion) · Daniel J Mankowitz (Technion) · Aviv Tamar (UC Berkeley) · Shie Mannor (Technion)

A Highly Efficient Gradient Boosting Decision Tree
Guolin Ke (Microsoft Research) · Qi Meng (Peking University) · Taifeng Wang (Microsoft Research) · Wei Chen (Microsoft Research) · Weidong Ma (Microsoft Research) · Tie-Yan Liu (Microsoft Research)

Adversarial Ranking for Language Generation
Dianqi Li (University of Washington) · Kevin Lin (University of Washington) · Xiaodong He (Microsoft Research, Redmond, WA) · Ming-ting Sun (University of Washington) · Zhengyou Zhang (Microsoft Research)

Regret Minimization in MDPs with Options without Prior Knowledge
Ronan Fruit (Inria Lille) · Matteo Pirotta (INRIA Lille-Nord Europe) · Alessandro Lazaric (INRIA Lille-Nord Europe) · Emma Brunskill (CMU)

Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Alireza Aghasi (IBM TJ Watson Research Center) · Nam Nguyen (IBM Thomas J. Watson Research Center) · Justin Romberg (Georgia Institute of Technology)

Graph Matching via Multiplicative Update Algorithm
Bo Jiang (Anhui University) · Jin Tang () · Bin Luo ()

Dynamic Importance Sampling for Anytime Bounds of the Partition Function
Qi Lou (UCI) · Rina Dechter (UCI) · Alexander Ihler (UC Irvine)

Is the Bellman residual a bad proxy?
Matthieu Geist (Université de Lorraine) · Bilal Piot (DeepMind) · Olivier Pietquin (DeepMind)

Generalization Properties of Learning with Random Features
Alessandro Rudi (INRIA) · Lorenzo Rosasco (University of Genova- MIT - IIT)

Differentially private Bayesian learning on distributed data
Mikko Heikkilä (University of Helsinki) · Eemil Lagerspetz (University of Helsinki) · Samuel Kaski (Aalto University) · Kana Shimizu (Waseda University) · Sasu Tarkoma (University of Helsinki) · Antti Honkela (University of Helsinki)

Learning to Compose Domain-Specific Transformations for Data Augmentation
Alexander Ratner (Stanford) · Henry Ehrenberg (Stanford University) · Zeshan Hussain (Stanford University) · Jared Dunnmon (Stanford University) · Christopher Ré (Stanford)

Wasserstein Learning of Deep Generative Point Process Models
SHUAI XIAO (Georgia Institute of Technology) · Mehrdad Farajtabar (Georgia Tech) · Xiaojing Ye (Georgia State University) · Junchi Yan (IBM Research - China) · Le Song (Georgia Institute of Technology) · Hongyuan Zha (Georgia Tech)

Ensemble Sampling
Xiuyuan Lu (Stanford University) · Benjamin Van Roy (Stanford University)

Language modeling with recurrent highway hypernetworks
Joseph Suarez (Stanford University)

Searching in the Dark: Practical SVRG Methods under Error Bound Conditions with Guarantee
Yi Xu (The University of Iowa) · Univ Qihang Lin (Univ Iowa, faculty) · Tianbao Yang (The University of Iowa)

Bayesian Compression for Deep Learning
Christos Louizos (University of Amsterdam) · Karen Ullrich (University of Amsterdam) · Max Welling (University of Amsterdam and University of California Irvine and CIFAR)

Streaming Sparse Gaussian Process Approximations
Thang D Bui (University of Cambridge) · Cuong Nguyen (University of Cambridge) · Richard E Turner (University of Cambridge)

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Akash Srivastava (University of Edinburgh) · Lazar Valkoz (University of Edinburgh) · Chris Russell (The Alan Turing Institute/ The University of Surrey) · Michael Gutmann (University of Edinburgh) · Charles Sutton (University of Edinburgh)

Sparse k-Means Embedding
Weiwei Liu (UTS) · Xiaobo Shen (NJUST) · Ivor Tsang (University of Technology, Sydney)

Utile Context Tree Weighting
Joao V Messias (Morpheus Labs) · Shimon Whiteson (Oxford University)

A Regularized Framework for Sparse and Structured Neural Attention
Vlad Niculae (Cornell University) · Mathieu Blondel (NTT)

Multi-output Polynomial Networks and Factorization Machines
Mathieu Blondel (NTT) · Vlad Niculae (Cornell University) · Takuma Otsuka (NTT Communication Science Labs) · Naonori Ueda (NTT Communication Science Laboratories)

Clustering Billions of Reads for DNA Data Storage
Cyrus Rashtchian (University of Washington) · Konstantin Makarychev (Microsoft) · Luis Ceze (Microsoft) · Karin Strauss (Microsoft Research) · Sergey Yekhanin (Microsoft) · Djordje Jevdjic (Microsoft Research) · Miklos Racz (Princeton University) · Siena Ang (Microsoft)

Multi-Objective Non-parametric Sequential Prediction
Guy Uziel (Technion) · Ran El-Yaniv (Technion)

A Universal Analysis of Large-Scale Regularized Least Squares Solutions
Ashkan Panahi (North Carolina State University) · Babak Hassibi (Caltech)

Deep Sets
Manzil Zaheer (Carnegie Mellon University) · Satwik Kottur (Carnegie Mellon University) · Siamak Ravanbakhsh (CMU/UBC) · Barnabas Poczos (Carnegie Mellon University) · Ruslan Salakhutdinov () · Alexander Smola (Amazon - We are hiring!)

ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
MILA Tegan Maharaj (MILA, Polytechnic Montreal) · Evan Racah () · Christopher J Beckham (Montreal Institute of Learning Algorithms) · Mr. Prabhat (LBL/NERSC) · Chris Pal (Montréal Institute for Learning Algorithms)

Process-constrained batch Bayesian optimisation
Pratibha Vellanki (Deakin University) · Santu Rana (Deakin University) · Sunil Gupta (Deakin University) · David Rubin () · Alessandra Sutti (Deakin University) · Thomas Dorin (Deakin University) · Murray Height () · Paul Sanders () · Svetha Venkatesh (Deakin University)

Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Ahmed Ibrahim (UCLA) · Mihaela van der Schaar (UCLA and Oxford University)

Spherical convolutions and their application in molecular modelling
Wouter Boomsma (University of Copenhagen) · Jes Frellsen (IT University of Copenhagen)

Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
Wenbing Huang (Tencent AI Lab) · Fuchun Sun (Tsinghua University) · Tong Zhang (The Australian National University) · Junzhou Huang (University of Texas at Arlington) · Mehrtash Harandi (Data61)

On Optimal Generalizability in Parametric Learning
Ahmad Beirami (Harvard University & MIT) · Meisam Razaviyayn (University of Southern California) · Shahin Shahrampour (Harvard University) · Vahid Tarokh (Harvard University)

Near Optimal Sketching of Low-Rank Tensor Regression
Xingguo Li (University of Minnesota) · Jarvis Haupt (University of Minnesota) · David Woodruff ()

Tractability in Structured Probability Spaces
Arthur Choi (UCLA) · Yujia Shen (UCLA) · Adnan Darwiche (UCLA)

Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit
Laurence Aitchison (University of Cambridge) · Lloyd Russell (University College London) · Adam Packer (University College London) · Jinyao Yan (Janelia Research Campus) · Philippe Castonguay (University of Montreal) · Michael Hausser (UCL) · Srinivas C Turaga (Janelia Research Campus, Howard Hughes Medical Institute)

Gaussian process based nonlinear latent structure discovery in multivariate spike train data
Anqi Wu (Princeton University) · Nicholas Roy (Princeton Neuroscience Institute) · Stephen Keeley (Princeton University) · Jonathan W Pillow (Princeton University)

Neural system identification for large populations separating "what" and "where"
David Klindt (CIN, University of Tuebingen) · Alexander Ecker (University of Tuebingen) · Thomas Euler (University of Tübingen) · Matthias Bethge (CIN, University Tübingen)

Certified Defenses for Data Poisoning Attacks
Jacob Steinhardt (Stanford University) · Pang Wei W Koh (Stanford University) · Percy Liang (Stanford University)

Eigen-Distortions of Hierarchical Representations
Alexander Berardino (New York University) · Valero Laparra (Universitat de València) · Johannes Ballé (Google Inc.) · Eero P Simoncelli (HHMI / New York University)

Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization
Yossi Arjevani (The Weizmann Institute)

Unsupervised Sequence Classification using Sequential Output Statistics
Yu Liu (SUNY Buffalo) · Jianshu Chen (Microsoft Research, Redmond, W) · Li Deng (Citadel)

Subset Selection under Noise
Chao Qian (University of Science and Technology of China) · Jing-Cheng Shi (Nanjing University) · Yang Yu () · Ke Tang (University of Science and Technology of China) · Zhi-Hua Zhou (Nanjing University)

Collecting Telemetry Data Privately
Bolin Ding (Microsoft) · Janardhan Kulkarni (Microsoft Research) · Sergey Yekhanin (Microsoft)

Concrete Dropout
Yarin Gal (University of Oxford) · Jiri Hron (University of Cambridge) · Alex Kendall (University of Cambridge)

Adaptive Batch Size for Safe Policy Gradients
Matteo Papini (Politecnico di Milano) · Matteo Pirotta (INRIA Lille-Nord Europe) · Marcello Restelli ()

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Marco Fraccaro (Technical University of Denmark (DTU)) · Simon Kamronn (Technical University of Denmark) · Ulrich Paquet () · Ole Winther (Technical University of Denmark)

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Jonathan Huggins (Massachusetts Institute of Technology) · Ryan Adams () · Tamara Broderick (MIT)

Bayesian GANs
Yunus Saatci (Uber AI Labs) · Andrew Wilson (Cornell University)

Off-policy evaluation for slate recommendation
Adith Swaminathan (Microsoft Research) · Akshay Krishnamurthy () · Alekh Agarwal (Microsoft Research) · Miro Dudik (Microsoft Research) · John Langford (Microsoft Research) · Damien Jose (Microsoft) · Imed Zitouni (Microsoft)

A multi-agent reinforcement learning model of common-pool resource appropriation
Julien Pérolat (CNRS) · Joel Leibo (DeepMind) · Vinicius Zambaldi (Deepmind) · Charles Beattie (DeepMind) · Karl Tuyls (University of Liverpool) · Thore Graepel (DeepMind)

On the Optimization Landscape of Tensor Decompositions
Rong Ge (Duke University) · Tengyu Ma (Facebook AI Research)

High-Order Attention Models for Visual Question Answering
Idan Schwartz (Technion) · Alexander Schwing (University of Illinois at Urbana-Champaign) · Tamir Hazan (Technion)

Sparse convolutional coding for neuronal assembly detection
Sven Peter (University Heidelberg) · Elke Kirschbaum (HCI/IWR, Heidelberg University) · Martin Both (Institute for Physiology and Pathophysiology Heidelberg University) · Intramural Lee Campbell (Intramural Research Program, National Institute on Drug Abuse) · Intramural Brandon Harvey (Intramural Research Program, National Institute on Drug Abuse) · Intramural Conor Heins (Intramural Research Program, National Institute on Drug Abuse) · Daniel Durstewitz (CIMH Heidelberg University) · Ferran Diego () · Fred Hamprecht (Heidelberg University)

Quantifying how much sensory information in a neural code is relevant for behavior
Giuseppe Pica (Istituto Italiano di Tecnologia) · Eugenio Piasini (Istituto Italiano di Tecnologia) · Houman Safaai (Harvard Medical School) · Caroline Runyan (University of Pittsburgh) · Christopher Harvey (Harvard Medical School) · Mathew Diamond (International School for Advanced Studies) · Christoph Kayser (University of Glasgow) · Tommaso Fellin (Istituto Italiano di Tecnologia) · Stefano Panzeri (Istituto Italiano di Tecnologia)

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Federico Monti (Università della Svizzera italiana) · Michael Bronstein (USI Lugano / Tel Aviv University / Intel) · Xavier Bresson (NTU)

Reducing Reparameterization Gradient Variance
Andrew Miller (Harvard) · Nick Foti (University of Washington) · Alexander D'Amour () · Ryan Adams ()

Visual Reference Resolution using Attention Memory for Visual Dialog
Paul Hongsuck Seo (POSTECH) · Andreas Lehrmann (Disney Research) · Bohyung Han (POSTECH) · Leonid Sigal (Disney Research / University of British Columbia)

Joint distribution optimal transportation for domain adaptation
Nicolas Courty (IRISA / University South Brittany) · Rémi Flamary (Université Côte d'Azur) · Amaury Habrard (University of Saint-Etienne, Univ. Lyon, Lab. H Curien, France) · Alain Rakotomamonjy (Université de Rouen Normandie)

Multiresolution Kernel Approximation for Gaussian Process Regression
Yi Ding (University of Chicago) · Risi Kondor (The University of Chicago) · Jonathan Eskreis-Winkler (University of Chicago)

Collapsed variational Bayes for Markov jump processes
Boqian Zhang (Purdue University) · Jiangwei Pan (Facebook) · Vinayak A Rao (Purdue University)

Universal consistency and minimax rates for online Mondrian Forest
Jaouad Mourtada (Ecole Polytechnique) · Stéphane Gaïffas (Ecole polytechnique) · Erwan Scornet (Ecole Polytechnique)

Efficiency Guarantees from Data
Darrell Hoy (Tremor Technologies) · Denis Nekipelov (University of Virginia) · Vasilis Syrgkanis (Microsoft Research)

Diving into the shallows: a computational perspective on large-scale shallow learning
SIYUAN MA (The Ohio State University) · Mikhail Belkin (Ohio State University)

End-to-end Differentiable Proving
Tim Rocktäschel (University of Oxford) · Sebastian Riedel (University College London)

Influence Maximization with $\epsilon$-Almost Submodular Threshold Function
Qiang Li (Institute of Computing Technol) · Wei Chen (Microsoft Research) · Institute of Computing Xiaoming Sun (Institute of Computing Technology, Chinese Academy of Sciences) · Institute of Computing Jialin Zhang (Institute of Computing Technology, Chinese Academy of Sciences)

Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs
Yunzhu Li (MIT) · Jiaming Song (Stanford University) · Stefano Ermon (Stanford)

Variational Laws of Visual Attention for Dynamic Scenes
Dario Zanca (University of Florence, University of Siena) · Marco Gori ()

Recursive Sampling for the Nystrom Method
Cameron Musco (Massachusetts Institute of Technology) · Christopher Musco (Mass. Institute of Technology)

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
Shixiang Gu (University of Cambridge and Max Planck Institute for Intelligent Systems ) · Tim Lillicrap (Google DeepMind) · Richard E Turner (University of Cambridge) · Zoubin Ghahramani (Uber and University of Cambridge) · Bernhard Schölkopf (MPI for Intelligent Systems) · Sergey Levine (UC Berkeley)

Dynamic Routing Between Capsules
Sara Sabour (Google) · Nicholas Frosst (Google) · Geoffrey E Hinton (Google & University of Toronto)

Incorporating Side Information by Adaptive Convolution
Di Kang (City University of Hong Kong) · Debarun Dhar (City University of Hong Kong) · Antoni Chan (City University of Hong Kong)

Conic Scan Coverage algorithm for nonparametric topic modeling
Mikhail Yurochkin (University of Michigan) · Aritra Guha (University of Michigan) · XuanLong Nguyen (University of Michigan)

FALKON: An Optimal Large Scale Kernel Method
Alessandro Rudi (INRIA) · Luigi Carratino (University of Genoa) · Lorenzo Rosasco (University of Genova- MIT - IIT)

Structured Generative Adversarial Networks
Hao Zhang (Carnegie Mellon University) · Zhijie Deng (Tsinghua University) · Xiaodan Liang (Carnegie Mellon University) · Jun Zhu (Tsinghua University) · Eric P Xing (Carnegie Mellon University)

Conservative Contextual Linear Bandits
Abbas Kazerouni (Stanford University) · Mohammad Ghavamzadeh (DeepMind) · Yasin Abbasi (Adobe Research) · Benjamin Van Roy (Stanford University)

Variational Memory Addressing in Generative Models
Jörg Bornschein (DeepMind) · Andriy Mnih () · Daniel Zoran (DeepMind) · Danilo Jimenez Rezende (Google DeepMind)

On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm
Masaaki Imaizumi (Institute of Statistical Mathematics / RIKEN) · Takanori Maehara (RIKEN AIP) · Kohei Hayashi (AIST / RIKEN)

Scalable Levy Process Priors for Spectral Kernel Learning
Phillip A Jang (Cornell University) · Andrew Loeb (Cornell University) · Matthew Davidow (Cornell University) · Andrew Wilson (Cornell University)

Deep Hyperspherical Learning
Weiyang Liu (Georgia Tech) · Institute of Automation yan-Ming Zhang (Institute of Automation, Chinese Academy of Sciences) · Xingguo Li (University of Minnesota) · Zhiding Yu (Carnegie Mellon University) · Bo Dai (Georgia Tech) · Tuo Zhao (Georgia Tech) · Le Song (Georgia Institute of Technology)

Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Dan Xu (University of Trento) · Wanli Ouyang (The Chinese University of Hong Kong) · Xavier Alameda-Pineda (INRIA) · Elisa Ricci () · Xiaogang Wang (The Chinese University of Hong Kong) · Nicu Sebe (University of Trento)

On-the-fly Operation Batching in Dynamic Computation Graphs
Graham Neubig (Carnegie Mellon University) · Yoav Goldberg (Bar-Ilan University) · Chris Dyer (DeepMind)

Nonlinear Acceleration of Stochastic Algorithms
Damien Scieur (INRIA - ENS) · Francis Bach (Inria) · Alexandre d'Aspremont (CNRS - Ecole Normale Supérieure)

Optimized Pre-Processing for Discrimination Prevention
Flavio Calmon (Harvard University) · Dennis Wei (IBM Research) · Karthikeyan Ramamurthy () · Bhanukiran Vinzamuri (IBM Research) · Kush R Varshney (IBM Research)

YASS: Yet Another Spike Sorter
Jin Hyung Lee (Columbia University) · David E Carlson (Duke University) · Hooshmand Shokri Razaghi (Columbia University) · Weichi Yao (Columbia University) · Georges A Goetz (Stanford University) · E.J. Chichilnisky (Stanford University) · Espen Hagen () · Gaute T. Einevoll (Norwegian University of Life Sciences) · Liam Paninski (Columbia University)

Independence clustering (without a matrix)
Daniil Ryabko (INRIA)

Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Artur Speiser (research center caesar, an associate of the Max Planck Society) · Jinyao Yan (Janelia Research Campus) · Evan Archer () · Lars Buesing (DeepMind) · Srinivas C Turaga (Janelia Research Campus, Howard Hughes Medical Institute) · Jakob H Macke (research center caesar, an associate of the Max Planck Society)

Adaptive Active Hypothesis Testing under Limited Information
Fabio Cecchi (Eindhoven University of Technology) · Nidhi Hegde (Nokia Bell Labs)

Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Ethan Elenberg (University of Texas at Austin) · Alexandros Dimakis (University of Texas, Austin) · Moran Feldman (Open University of Israel) · Amin Karbasi (Yale)

Successor Features for Transfer in Reinforcement Learning
Andre Barreto (DeepMind) · Will Dabney (DeepMind) · Remi Munos (DeepMind) · Jonathan Hunt (DeepMind) · Tom Schaul (DeepMind) · David Silver (DeepMind) · Hado van Hasselt (DeepMind)

Counterfactual Fairness
Matt Kusner (Alan Turing Institute) · Joshua Loftus (The Alan Turing Institute) · Chris Russell (The Alan Turing Institute/ The University of Surrey) · Ricardo Silva (University College London)

Prototypical Networks for Few-shot Learning
Jake Snell (University of Toronto) · Kevin Swersky (Google Brain) · Richard Zemel (University of Toronto)

Triple Generative Adversarial Nets
Chongxuan LI (Tsinghua University) · Kun Xu () · Jun Zhu (Tsinghua University) · Bo Zhang (Tsinghua University)

Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression
Shinji Ito (NEC Coorporation) · Akihiro Yabe () · Ken-Ichi Kawarabayashi () · Naonori Kakimura () · Takuro Fukunaga (National Institute of Informatics) · Daisuke Hatano (National Institute of Informatics) · Hanna Sumita (National Institute of Informatics)

Mapping distinct timescales of functional interactions among brain networks
Mali Sundaresan (Indian Institute of Science) · Arshed Nabeel (Indian Institute of Science, Bangalore) · Devarajan Sridharan ()

Multi-Armed Bandits with Metric Movement Costs
Tomer Koren (Google) · Roi Livni (Princeton) · Yishay Mansour (Tel Aviv University)

Learning A Structured Optimal Bipartite Graph for Co-Clustering
Feiping Nie (University of Texas Arlington) · Xiaoqian Wang (University of Pittsburgh) · Heng Huang (Computer Science and Engineering University of Texas at Arlington)

Learning Low-Dimensional Metrics
Blake Mason (University of Wisconsin - Madison) · Lalit Jain (University of Michigan) · Robert Nowak (University of Wisconsion-Madison)

The Marginal Value of Adaptive Gradient Methods in Machine Learning
Ashia C Wilson (UC Berkeley) · Rebecca Roelofs (UC Berkeley) · Mitchell Stern (UC Berkeley) · Nati Srebro (TTI-Chicago) · Benjamin Recht (UC Berkeley)

Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
Bikash Joshi (University of Grenoble Alpes) · Massih-Reza Amini (University Grenoble Alps) · Ioannis Partalas (Expedia LPS Geneva) · Franck Iutzeler (Univ. Grenoble Alpes) · Yury Maximov (Los Alamos National Laboratory and Skolkovo Institute of Science and Technology)

Deconvolutional Paragraph Representation Learning
Yizhe Zhang (Duke University) · Dinghan Shen (Duke University) · Guoyin Wang (Duke University) · zhe Gan (duke) · Ricardo Henao (Duke University) · Lawrence Carin (Duke University)

Random Permutation Online Isotonic Regression
Wojciech Kotlowski (Poznan University of Technology) · Wouter Koolen (Centrum Wiskunde & Informatica) · Alan Malek (MIT)

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot () · Vinicius Zambaldi (Deepmind) · Audrunas Gruslys (Google DeepMind) · Angeliki Lazaridou (DeepMind) · karl Tuyls (DeepMind) · Julien Perolat () · David Silver (DeepMind) · Thore Graepel (DeepMind)

Inverse Filtering for Hidden Markov Models
Robert Mattila (KTH Royal Institute of Technology) · Cristian Rojas (KTH Royal Institute of Technology) · Vikram Krishnamurthy (Cornell University) · Bo Wahlberg (KTH Royal Inst. of Technology)

Non-parametric Neural Networks
Andreas Lehrmann (Disney Research) · Leonid Sigal (Disney Research / University of British Columbia)

Learning Active Learning from Data
Ksenia Konyushkova (EPFL) · Raphael Sznitman (University of Bern) · Pascal Fua ()

VAE Learning via Stein Variational Gradient Descent
Yuchen Pu (Duke University) · zhe Gan (duke) · Ricardo Henao (Duke University) · Chunyuan Li (Duke University) · Shaobo Han (Duke University) · Lawrence Carin (Duke University)

Deep adversarial neural decoding
Yağmur Güçlütürk (Radboud University) · Umut Güçlü (Donders Institute) · Katja Seeliger (Donders Institute for Brain, Cognition and Behaviour) · Sander Bosch (Radboud University) · Rob van Lier (Donders Institute for Brain, Cognition and Behaviour, Radboud University) · Marcel A. J. van Gerven (Radboud Universiteit)

Efficient Use of Limited-Memory Resources to Accelerate Linear Learning
Celestine Dünner (IBM Research) · Thomas Parnell (IBM Research) · Martin Jaggi (EPFL)

Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks
Prateep Bhattacharjee (Indian Institute of Technology Madras) · Sukhendu Das (IIT Madras)

Sobolev Training for Neural Networks
Wojciech M. Czarnecki (DeepMind) · Simon Osindero (DeepMind) · Max Jaderberg (DeepMind) · Grzegorz M Swirszcz (DeepMind @ Google) · Razvan Pascanu (Google DeepMind)

Multi-Information Source Optimization
Matthias Poloczek (Cornell University) · Jialei Wang (Cornell) · Peter Frazier (Cornell University)

Deep Reinforcement Learning from Human Preferences
Paul F Christiano (OpenAI) · Jan Leike (DeepMind) · Tom Brown (OpenAI) · Miljan Martic (DeepMind) · Shane Legg (DeepMind) · Dario Amodei (OpenAI)

On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
Arturs Backurs (MIT) · Piotr Indyk (MIT) · Ludwig Schmidt (MIT)

Policy Gradient With Value Function Approximation For Collective Multiagent Planning
Duc Thien Nguyen (Singapore Management University) · Akshat Kumar (Singapore Management Universit) · Hoong Chuin Lau (Singapore Management University)

Adversarial Symmetric Variational Autoencoder
Yuchen Pu (Duke University) · Weiyao Wang (Duke University) · Ricardo Henao (Duke University) · Liqun Chen (Duke University) · zhe Gan (duke) · Chunyuan Li (Duke University) · Lawrence Carin (Duke University)

Tensor encoding and decomposition of brain connectomes with application to tractography evaluation
Cesar F Caiafa (Indiana University) · Olaf Sporns (Department of Psychological and Brain Sciences - Indiana University) · Andrew Saykin (IUPUI) · Franco Pestilli (Indiana University)

A Minimax Optimal Algorithm for Crowdsourcing
Richard Combes (Centrale-Supelec) · Thomas Bonald (Telecom ParisTech)

Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
Emmanouil Platanios (Carnegie Mellon University) · Hoifung Poon (Microsoft Research) · Tom M Mitchell (Carnegie Mellon University) · Eric J Horvitz (Microsoft Research)

A Decomposition of Forecast Error in Prediction Markets
Miro Dudik (Microsoft Research) · Sebastien Lahaie (Google) · Ryan M Rogers (University of Pennsylvania) · Jennifer Wortman Vaughan (Microsoft Research)

Safe Adaptive Importance Sampling
Sebastian Stich (EPFL) · Anant Raj (Max Planck Institute for Intelligent Systems) · Martin Jaggi (EPFL)

Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
Anirudh Goyal ALIAS PARTH GOYAL (Université de Montréal) · Nan Ke (MILA, École Polytechnique de Montréal) · Surya Ganguli (Stanford) · Yoshua Bengio (U. Montreal)

Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication
Qian Yu (University of Southern Califor) · Mohammad Maddah-Ali (Nokia Bell Labs) · Salman Avestimehr (USC)

Unsupervised Learning of Disentangled Representations from Video
Emily Denton (New York University) · vighnesh Birodkar (New York University)

Federated Multi-Task Learning
Virginia Smith (UC Berkeley) · Maziar Sanjabi (University of California, Los Angeles) · Chao-Kai Chiang (University of Southern California) · Ameet S Talwalkar (UCLA)

Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?
Cameron Musco (Massachusetts Institute of Technology) · David Woodruff ()

The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
Arun Suggala (Carnegie Mellon University) · Mladen Kolar (University of Chicago) · Pradeep Ravikumar (Carnegie Mellon University)

Improved Graph Laplacian via Geometric Self-Consistency
Dominique Joncas (Google) · Marina Meila (University of Washington) · James McQueen (University of Washington)

Dual Path Networks
Yunpeng Chen (National University of Singapore) · Jianan Li (Beijing Institute of Technology) · Huaxin Xiao (NUDT) · Xiaojie Jin (National University of Singapore) · Shuicheng Yan (Qihoo 360 AI Institute) · Jiashi Feng (National University of Singapore)

Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers
Cong Fang (Peking University) · Feng Cheng (Peking University) · Zhouchen Lin (Peking University)

A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
Qinliang Su (Duke University) · xuejun Liao () · Lawrence Carin (Duke University)

DisTraL: Robust multitask reinforcement learning
Yee Teh (DeepMind) · Victor Bapst (DeepMind) · Razvan Pascanu (Google DeepMind) · Nicolas Heess (Google DeepMind) · John Quan (Google DeepMind) · James Kirkpatrick (Google DeepMind) · Wojciech M. Czarnecki (DeepMind) · Raia Hadsell (DeepMind)

Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions
Mukadder Baltaoglu (Cornell University) · Lang Tong (Cornell University) · Qing Zhao (Cornell University)

Trimmed Density Ratio Estimation
Song Liu (The Institute of Statistical Mathematics) · Akiko Takeda (The Institute of Statistical Mathematics / RIKEN) · Taiji Suzuki () · Kenji Fukumizu (Institute of Statistical Mathematics)

Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems
Ingmar Kanitscheider (UT Austin) · Ila Fiete ()

Visual Interaction Networks
Nicholas Watters (Google DeepMind) · Daniel Zoran (DeepMind) · Theophane Weber (DeepMind) · Peter Battaglia (DeepMind) · Razvan Pascanu (Google DeepMind) · Andrea Tacchetti (MIT)

Reconstruct & Crush Network
Erinc Merdivan (Austrian Institute of Tech.) · Mohammad Reza Loghmani (TU Wien) · Matthieu Geist (Université de Lorraine)

Streaming Robust Submodular Maximization:A Partitioned Thresholding Approach
Slobodan Mitrovic (EPFL) · Ilija Bogunovic (EPFL Lausanne) · Ashkan Norouzi-Fard (EPFL) · Jakub M Tarnawski (EPFL) · Volkan Cevher (EPFL)

Simple strategies for recovering inner products from coarsely quantized random projections
Ping Li (Rugters University) · Martin Slawski ()

Discovering Potential Influence via Information Bottleneck
Weihao Gao (UIUC) · Sreeram Kannan () · Hyeji Kim (University of Illinois Urbana-Champaign) · Sewoong Oh (UIUC) · Pramod Viswanath (UIUC)

Doubly Stochastic Variational Inference for Deep Gaussian Processes
Hugh Salimbeni (Imperial College London) · Marc Deisenroth (Imperial College London)

Ranking Data with Continuous Labels through Oriented Recursive Partitions
Stéphan Clémençon (Telecom ParisTech) · Mastane Achab (Télécom ParisTech)

Scalable Model Selection for Belief Networks
Zhao Song (Duke University) · Yusuke Muraoka () · Ryohei Fujimaki (NEC Data Science Research Laboratories) · Lawrence Carin (Duke University)

Targeting EEG/LFP Synchrony with Neural Nets
Yitong Li (Duke University) · David E Carlson (Duke University) · Lawrence Carin (Duke University)

Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
Sanjiban Choudhury (Carnegie Mellon University) · Shervin Javdani (Carnegie Mellon University) · Siddhartha Srinivasa (Carnegie Mellon University) · Sebastian Scherer (Carnegie Mellon University)

Non-Stationary Spectral Kernels
Sami Remes (Aalto University) · Markus Heinonen (Aalto University) · Samuel Kaski (Aalto University)

Overcoming Catastrophic Forgetting by Incremental Moment Matching
Sang-Woo Lee (Seoul National University) · Jin-Hwa Kim (Seoul National University) · Jaehyun Jun (Seoul National University) · Jung-Woo Ha (Clova, NAVER Corp.) · Byoung-Tak Zhang (Seoul National University & Surromind Robotics)

Balancing information exposure in social networks
Kiran Garimella (Aalto University) · Aristides Gionis (Aalto University) · Nikos Parotsidis (University of Rome Tor Vergata) · Nikolaj Tatti (Aalto University)

SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud
Zahra Ghodsi (New York University) · Tianyu Gu (NYU) · Siddharth Garg (NYU)

Query Complexity of Clustering with Side Information
Barna Saha (University of Massachusetts Amherst) · Arya Mazumdar (University of Massachusetts Amherst)

QMDP-Net: Deep Learning for Planning under Partial Observability
Peter Karkus (NUS) · David Hsu (National University of Singapore) · Wee Sun Lee (National University of Singapore)

Robust Optimization for Non-Convex Objectives
Yaron Singer (Harvard University) · Robert S Chen (Harvard University) · Vasilis Syrgkanis (Microsoft Research) · Brendan Lucier (Microsoft Research)

Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation
Christian Borgs (Microsoft Research New England) · Jennifer Chayes (Microsoft Research) · Christina Lee (Microsoft Research) · Devavrat Shah (Massachusetts Institute of Technology)

Adaptive Classification for Prediction Under a Budget
Feng Nan (Boston University) · Venkatesh Saligrama (Boston University)

Convergence rates of a partition based Bayesian multivariate density estimation method
Linxi Liu (Columbia University) · Dangna Li (Stanford University) · Wing Hung Wong (Stanford university)

Affine-Invariant Online Optimization
Tomer Koren (Google) · Roi Livni (Princeton)

Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization
Omar El Housni (Columbia University) · Vineet Goyal (Columbia University)

A unified approach to interpreting model predictions
Scott M Lundberg (University of Washington) · Su-In Lee (University of Washington)

Stochastic Approximation for Canonical Correlation Analysis
Raman Arora (Johns Hopkins University) · Teodor Vanislavov Marinov (Johns Hopkins University) · Poorya Mianjy (Johns Hopkins University)

Investigating the learning dynamics of deep neural networks using random matrix theory
Jeffrey Pennington (Google Brain) · Samuel Schoenholz (Google Brain) · Surya Ganguli (Stanford)

Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions
Maria-Florina Balcan (Carnegie Mellon University) · Hongyang Zhang (Carnegie Mellon University)

Scalable Variational Inference for Dynamical Systems
Stefan Bauer (ETH Zürich) · Nico S Gorbach (Swiss Federal Institute of Technology Zurich (ETHZ)) · Joachim M Buhmann (ETH Zurich)

Context Selection for Embedding Models
Liping Liu (Tufts University) · Francisco Ruiz () · David Blei (Columbia University)

Working hard to know your neighbor's margins: Local descriptor learning loss
Anastasiia Mishchuk (Szkocka Research Group, Ukraine) · Dmytro Mishkin (Czech Technical University in Prague) · Filip Radenovic (Visual Recognition Group, CTU in Prague) · Jiri Matas (Czech Technical University)

Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex
Chaobing Song (Tsinghua University) · Shaobo Cui (Tsinghua University) · Shu-Tao Xia (Tsinghua University) · Yong Jiang (Tsinghua-Berkeley Shenzhen Institute)

Multi-Task Learning for Contextual Bandits
Aniket Anand Deshmukh (University of Michigan, Ann Arbor) · Urun Dogan (Microsoft) · Clay Scott (University of Michigan)

Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Xin Dong (Nanyang Technological Univ) · Shangyu Chen (Nanyang Technological Unvi) · Sinno Pan (NTU)

Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds
Yuanyuan Liu (The Chinese University of Hong Kong) · Fanhua Shang (The Chinese University of Hong Kong) · James Cheng (The Chinese University of Hong Kong) · Hong Cheng (The Chinese University of Hong Kong) · Licheng Jiao (Xidian University)

Selective Classification for Deep Neural Networks
Yonatan Geifman (Technion) · Ran El-Yaniv (Technion)

Minimax Estimation of Bandable Precision Matrices
Addison Hu (Yale University) · Sahand Negahban (Yale University)

Monte-Carlo Tree Search by Best Arm Identification
Emilie Kaufmann (CNRS & CRIStAL (SequeL)) · Wouter Koolen (Centrum Wiskunde & Informatica)

Group Additive Structure Identification for Kernel Nonparametric Regression
Chao Pan (Purdue University) · Michael Zhu (Purdue University)

Fast, Sample-Efficient Algorithms for Structured Phase Retrieval
Gauri Jagatap (Iowa State University) · Chinmay Hegde (Iowa State University)

Hash Embeddings for Efficient Word Representations
Dan Tito Svenstrup (DTU) · Jonas Hansen (Findzebra) · Ole Winther (Technical University of Denmark)

Online Learning for Multivariate Hawkes Processes
Yingxiang Yang (UIUC) · Jalal Etesami (UIUC) · Niao He (UIUC) · Negar Kiyavash (UIUC)

Maximum Margin Interval Trees
Alexandre Drouin (Université Laval + Element AI) · Toby D Hocking (McGill Genome Center, McGill University) · Francois Laviolette (Université Laval)

DropoutNet: Addressing Cold Start in Recommender Systems
Maksims Volkovs (layer6.ai) · Guangwei Yu (Layer6 AI, University of Toronto) · Tomi Poutanen ()

A simple neural network module for relational reasoning
Adam Santoro (DeepMind) · David Raposo (DeepMind) · David Barrett (DeepMind) · Mateusz Malinowski (DeepMind) · Razvan Pascanu (Google DeepMind) · Peter Battaglia (DeepMind) · Tim Lillicrap (Google DeepMind)

Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
Jianshu Chen (Microsoft Research, Redmond, W) · Chong Wang () · Lin Xiao (Microsoft Research) · Ji He (University Washington) · Lihong Li (Microsoft Research) · Li Deng (Citadel)

Online Reinforcement Learning in Stochastic Games
Chen-Yu Wei (Academia Sinica) · Yi-Te Hong (Dept. of Computer Science, National Taiwan University) · Chi-Jen Lu (Academia Sinica)

Position-based Multiple-play Multi-armed Bandit Problem with Unknown Position Bias
Junpei Komiyama (The University of Tokyo) · Junya Honda (The University of Tokyo) · Akiko Takeda (The Institute of Statistical Mathematics / RIKEN)

Active Exploration for Learning Symbolic Representations
Garrett Andersen (PROWLER.io) · George Konidaris (Brown University)

Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
Andrei-Cristian Barbos (University of Bordeaux) · Francois Caron () · Jean-François Giovannelli (University of Bordeaux) · Arnaud Doucet (Oxford)

Fair Clustering Through Fairlets
Flavio Chierichetti (Sapienza University) · Ravi Kumar (Google) · Silvio Lattanzi (Google) · Sergei Vassilvitskii (Google)

Polynomial time algorithms for dual volume sampling
Chengtao Li (MIT) · Stefanie Jegelka (MIT) · Suvrit Sra (MIT)

Hindsight Experience Replay
Marcin Andrychowicz (OpenAI) · Filip Wolski (OpenAI) · Alex Ray (OpenAI) · Jonas Schneider (OpenAI) · Rachel Fong (OpenAI) · Peter Welinder (OpenAI) · Bob McGrew (OpenAI) · Josh Tobin (OpenAI) · OpenAI Pieter Abbeel (OpenAI, UC Berkeley) · Wojciech Zaremba (OpenAI)

Stochastic and Adversarial Online Learning without Hyperparameters
Ashok Cutkosky (Stanford University) · Kwabena A Boahen (Stanford University)

Teaching Machines to Describe Images with Natural Language Feedback
huan ling (university of toronto) · Sanja Fidler (University of Toronto)

Perturbative Black Box Variational Inference
Cheng Zhang (Disney Research) · Robert Bamler (Disney Research) · Manfred Opper (TU Berlin) · Stephan Mandt (Disney Research)

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Alex Lamb (UMontreal (MILA)) · Devon Hjelm (MILA) · Yaroslav Ganin (Université de Montréal) · Joseph Paul Cohen (Montreal Institute for Learning Algorithms) · Aaron C Courville (U. Montreal) · Yoshua Bengio (U. Montreal)

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Charles Ruizhongtai Qi (Stanford University) · Li Yi (Stanford University) · Hao Su (Stanford) · Leonidas J Guibas (stanford.edu)

Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
Hyeonwoo Noh (POSTECH) · Tackgeun You (Pohang University of Science and Technology) · Jonghwan Mun (POSTECH) · Bohyung Han (POSTECH)

Learning Graph Embeddings with Embedding Propagation
Alberto Garcia Duran (NEC Europe) · Mathias Niepert (NEC Labs Europe)

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Zhenwen Dai (Amazon) · Mauricio A. Álvarez (University of Sheffield) · Neil Lawrence (Amazon.com)

A-NICE-MC: Adversarial Training for MCMC
Jiaming Song (Stanford University) · Shengjia Zhao (Stanford University) · Stefano Ermon (Stanford)

Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models
Rishit Sheth (Tufts University) · Roni Khardon (Tufts University)

Real-Time Bidding with Side Information
arthur flajolet (MIT) · Patrick Jaillet (Massachusetts Institute of Technology)

Saliency-based Sequential Image Attention with Multiset Prediction
Sean Welleck (NYU) · Kyunghyun Cho (NYU) · Zheng Zhang (Shanghai New York Univeristy)

Variational Inference for Gaussian Process Models with Linear Complexity
Ching-An Cheng (Georgia Tech) · Byron Boots (Georgia Tech / Google Brain)

K-Medoids For K-Means Seeding
James Newling (Idiap Research Institute & EPFL) · François Fleuret (Idiap Research Institute)

Identifying Outlier Arms in Multi-Armed Bandit
Honglei Zhuang (University of Illinois) · Chi Wang (Microsoft Research) · Yifan Wang (Tsinghua University)

Online Learning with Transductive Regret
Scott Yang (D. E. Shaw & Co.) · Mehryar Mohri (Courant Institute and Google)

Riemannian approach to batch normalization
Minhyung Cho (Gracenote) · Jaehyung Lee (Gracenote)

Self-supervised Learning of Motion Capture
Hsiao-Yu Tung (Carnegie Mellon University) · Hsiao-Wei Tung (University of Pittsburgh) · Ersin Yumer (Adobe Research) · Katerina Fragkiadaki ()

Triangle Generative Adversarial Networks
zhe Gan (duke) · Liqun Chen (Duke University) · Weiyao Wang (Duke University) · Yuchen Pu (Duke University) · Yizhe Zhang (Duke university) · Lawrence Carin (Duke University)

Preserving Proximity and Global Ranking for Node Embedding
Yi-An Lai (National Taiwan University) · Chin-Chi Hsu (Academia Sinica) · Wen Hao Chen (National Taiwan University) · Mi-Yen Yeh (Academia Sinica) · Shou-De Lin (National Taiwan University)

Bayesian Optimization with Gradients
Jian Wu (AQR Capital Management) · Matthias Poloczek (Cornell University) · Andrew Wilson (Cornell University) · Peter Frazier (Cornell University)

Second-order Optimization in Deep Reinforcement Learning using Kronecker-factored Approximation
Yuhuai Wu (University of Toronto) · Elman Mansimov (New York University) · Roger Grosse (University of Toronto) · Shun Liao (University of Toronto) · Jimmy Ba (University of Toronto / Vector Institute)

Renyi Differential Privacy Mechanisms for Posterior Sampling
Joseph Geumlek (UCSD) · Shuang Song (UC San Diego) · Kamalika Chaudhuri (UCSD)

Online Learning with a Hint
Ofer Dekel (Microsoft Research) · arthur flajolet (MIT) · Nika Haghtalab (Carnegie Mellon University) · Patrick Jaillet (Massachusetts Institute of Technology)

Identification of Gaussian Process State Space Models
Stefanos Eleftheriadis (PROWLER.io) · Tom Nicholson (PROWLER.IO) · Marc Deisenroth (Imperial College London) · James Hensman (PROWLER.io)

Robust Imitation of Diverse Behaviors
Ziyu Wang (Deepmind) · Josh Merel (DeepMind) · Scott Reed (Google DeepMind) · Nando de Freitas (DeepMind) · Gregory Wayne (Google DeepMind) · Nicolas Heess (Google DeepMind)

Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Xiangru Lian (University of Rochester) · Ce Zhang (ETH Zurich) · Huan Zhang () · Cho-Jui Hsieh (UC Davis) · Wei Zhang (IBM T.J.Watson Research Center) · Ji Liu (University of Rochester)

Local Aggregative Games
Vikas Garg (MIT) · Tommi Jaakkola (MIT)

A Sample Complexity Measure with Applications to Learning Optimal Auctions
Vasilis Syrgkanis (Microsoft Research)

Thinking Fast and Slow with Deep Learning and Tree Search
Thomas Anthony (UCL) · Zheng Tian (UCL) · David Barber (University College London)

EEG-GRAPH: A Factor Graph Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms
Yogatheesan Varatharajah (University of Illinois at Urbana Champaign) · Min Jin Chong (University of Illinois at Urbana-Champaign) · Krishnakant Saboo () · Brent Berry (Mayo Clinic) · Benjamin Brinkmann (Mayo Clinic) · Gregory Worrell (Mayo Clinic, Rochester) · Ravishankar Iyer ()

Improving the Expected Improvement Algorithm
Chao Qin (Columbia University) · Diego Klabjan (Northwestern University) · Daniel Russo (Stanford University)

Hybrid Reward Architecture for Reinforcement Learning
Harm Van Seijen (Microsoft Research) · Romain Laroche () · Mehdi Fatemi () · Joshua Romoff (McGill University)

Approximate Supermodularity Bounds for Experimental Design
Luiz Chamon (University of Pennsylvania) · Alejandro Ribeiro (University of Pennsylvania)

Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification
Jinseok Nam (TU Darmstadt) · Eneldo Mencía (TU Darmstadt) · Hyunwoo J Kim (University of Wisconsin-Madison) · Johannes Fürnkranz (TU Darmstadt)

AdaGAN: Boosting Generative Models
Ilya Tolstikhin (MPI for Intelligent Systems) · Sylvain Gelly (Google Brain) · Olivier Bousquet (Google) · Carl-Johann SIMON-GABRIEL (Max Planck Institute for Intelligent Systems) · Bernhard Schölkopf (MPI for Intelligent Systems)

Straggler Mitigation in Distributed Optimization Through Data Encoding
Can Karakus (UCLA) · Yifan Sun () · Suhas Diggavi (UCLA) · Wotao Yin (University of California, Los Angeles)

Multi-View Decision Processes
Christos Dimitrakakis (Chalmers / Harvard / Lille / Oslo) · David C Parkes (Harvard University ) · Goran Radanovic (Harvard) · Paul Tylkin (Harvard University)

A Greedy Approach for Budgeted Maximum Inner Product Search
Hsiang-Fu Yu (U Texas) · Cho-Jui Hsieh (UC Davis) · Qi Lei (Institute for Computational Engineering and Sciences, University of Texas at Austin) · Inderjit S Dhillon (University of Texas at Austin)

SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks
Kyuhong Shim (Seoul National University) · Minjae Lee (Seoul National University) · Iksoo Choi (Seoul National University) · Yoonho Boo (Seoul National University) · Wonyong Sung (Seoul National University)

Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
Caglar Gulcehre (Deepmind) · Francis Dutil (MILA) · Adam Trischler (Microsoft) · Yoshua Bengio (U. Montreal)

Task-based End-to-end Model Learning in Stochastic Optimization
Priya Donti (Carnegie Mellon University) · J. Zico Kolter (Carnegie Mellon University) · Brandon Amos (Carnegie Mellon University)

Towards Understanding Adversarial Learning for Joint Distribution Matching
Chunyuan Li (Duke University) · Hao Liu (Nanjing University) · Ricardo Henao (Duke University) · Liqun Chen (Duke University) · Yuchen Pu (Duke University) · Changyou Chen (University at Buffalo) · Lawrence Carin (Duke University)

Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting
Yue Wang (Beijing Jiaotong University)

On the Complexity of Learning Neural Networks
Le Song (Georgia Institute of Technology) · Santosh Vempala (Georgia Tech) · John Wilmes (Georgia Institute of Technology) · Bo Xie (Georgia Tech)

Hierarchical Implicit Models and Likelihood-Free Variational Inference
Dustin Tran (Columbia University & OpenAI) · Rajesh Ranganath (Princeton University) · David Blei (Columbia University)

Improved Semi-supervised Learning with GANs using Manifold Invariances
Abhishek Kumar (IBM Research) · Prasanna Sattigeri (IBM Research) · Tom Fletcher (University of Utah)

Approximation and Convergence Properties of Generative Adversarial Learning
Shuang Liu (University of California, San Diego) · Olivier Bousquet (Google) · Kamalika Chaudhuri (UCSD)

From Bayesian Sparsity to Gated Recurrent Nets
Hao He (PekingUniversity) · Bo Xin (Microsoft Research) · David Wipf (Microsoft Research)

Min-Max Propagation
Christopher Srinivasa (University of Toronto/RBC Research Institute) · Inmar Givoni () · Siamak Ravanbakhsh (CMU/UBC) · Brendan J Frey (Deep Genomics, Vector Institute, Univ. Toronto)

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall (University of Cambridge) · Yarin Gal (University of Oxford)

Gradient descent GAN optimization is locally stable
Vaishnavh Nagarajan (Carnegie Mellon University) · J. Zico Kolter (Carnegie Mellon University)

Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
Arash Vahdat (D-Wave Systems Inc.)

Dualing GANs
Yujia Li (University of Toronto) · Alexander Schwing (University of Illinois at Urbana-Champaign) · Kuan-Chieh Wang (University of Toronto) · Richard Zemel (University of Toronto)

Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Xingjian Shi (HKUST) · Hao Wang (HKUST) · Zhihan Gao (HKUST) · Leonard Lausen (HKUST) · Dit-Yan Yeung (HKUST, Hong Kong) · Wang-chun WOO (HKO) · Wai-kin Wong (HKO)

Do Deep Neural Networks Suffer from Crowding?
Anna Volokitin (ETH Zurich) · Gemma Roig (Massachusetts Institute of Technology) · Tomaso A Poggio (MIT)

Learning from Complementary Labels
Takashi Ishida (Sumitomo Mitsui Asset Management, The University of Tokyo, RIKEN) · Masashi Sugiyama (RIKEN / University of Tokyo) · Gang Niu (The University of Tokyo)

More powerful and flexible rules for online FDR control with memory and weights
Aaditya Ramdas (University of California, Berkeley) · Fanny Yang (University of California, Berkeley) · Martin Wainwright (UC Berkeley) · Michael Jordan (UC Berkeley)

Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
Anton Mallasto (University of Copenhagen) · Aasa Feragen (University of Copenhagen)

Discriminative State Space Models
Vitaly Kuznetsov (Google Research) · Mehryar Mohri (Courant Institute and Google)

On Fairness and Calibration
Geoff Pleiss (Cornell University) · Manish Raghavan (Cornell University) · Felix Wu () · Jon Kleinberg (Cornell University) · Kilian Q Weinberger (Cornell University)

Imagination-Augmented Agents for Deep Reinforcement Learning
Sébastien Racanière (Google DeepMind) · David Reichert (DeepMind) · Theophane Weber (DeepMind) · Oriol Vinyals (Google DeepMind) · Daan Wierstra (DeepMind Technologies) · Lars Buesing (DeepMind) · Peter Battaglia (DeepMind) · Razvan Pascanu (Google DeepMind) · Yujia Li (DeepMind) · Nicolas Heess (Google DeepMind) · Arthur Guez (Google) · Danilo Jimenez Rezende (Google DeepMind) · Adrià Puigdomènech Badia (Google DeepMind) · David Silver (DeepMind)

Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Marcel Nonnenmacher (Research center caesar) · Srinivas C Turaga (Janelia Research Campus, Howard Hughes Medical Institute) · Jakob H Macke (research center caesar, an associate of the Max Planck Society)

Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann (Carnegie Mellon University) · Tor Lattimore (DeepMind) · Emma Brunskill (Stanford University)

Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra
John Halloran (University of California, Davis) · University of California David Rocke (University of California, Davis)

Asynchronous Parallel Coordinate Minimization for MAP Inference
Ofer Meshi (Google) · Alexander Schwing (University of Illinois at Urbana-Champaign)

Multiscale Quantization for Fast Similarity Search
Xiang Wu (Google) · Ruiqi Guo (Google) · Ananda Theertha Suresh (Google) · Daniel Holtmann-Rice (Google Inc) · David Simcha (Google) · Felix Yu (Google Research) · Sanjiv Kumar (Google Research)

Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space
Liwei Wang (University of Illinois at Urbana–Champaign) · Alexander Schwing (University of Illinois at Urbana-Champaign) · Svetlana Lazebnik ()

Improved Training of Wasserstein GANs
Ishaan Gulrajani (Google) · Faruk Ahmed (MILA) · Martin Arjovsky (New York University) · Vincent Dumoulin (Université de Montréal) · Aaron C Courville (U. Montreal)

Optimally Learning Populations of Parameters
Kevin Tian (Stanford University) · Weihao Kong (Stanford University) · Gregory Valiant (Stanford University)

Clustering with Noisy Queries
Barna Saha (University of Massachusetts Amherst) · Arya Mazumdar (University of Massachusetts Amherst)

Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods
Veeranjaneyulu Sadhanala (CMU) · Yu-Xiang Wang (CMU / Amazon AI) · James Sharpnack () · Ryan Tibshirani (Carnegie Mellon University)

Training Quantized Nets: A Deeper Understanding
Hao Li (University of Maryland, College Park) · Soham De (University of Maryland, College Park) · Zheng Xu (University of Maryland, College Park) · Christoph Studer (Cornell University) · Hanan Samet (University of Maryland at College Park) · Tom Goldstein (University of Maryland)

Permutation-based Causal Inference Algorithms with Interventions
Yuhao Wang (MIT) · Liam Solus (KTH Royal Institute of Technology) · Karren Yang (MIT) · Caroline Uhler (MIT)

Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Kristjan Greenewald (University of Michigan) · Seyoung Park (Yale University) · Shuheng Zhou (University of Michigan) · Alexander Giessing (University of Michigan)

Gradient Methods for Submodular Maximization
Hamed Hassani (UPenn) · Mahdi Soltanolkotabi (University of Southern california) · Amin Karbasi (Yale)

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
Ahmet Alacaoglu (EPFL) · Quoc Tran Dinh (Department of Statistics and Operations Research, UNC, North Carolina) · Olivier Fercoq (Telecom ParisTech) · Volkan Cevher (EPFL)

Maximizing the Spread of Influence from Training Data
Eric Balkanski (Harvard University) · Nicole Immorlica (Microsoft Research) · Yaron Singer (Harvard University)

Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos
gerasimos Palaiopanos (SUTD) · Ioannis Panageas (MIT) · Georgios Piliouras (Singapore University of Technology and Design)

Learning Neural Representations of Human Cognition across Many fMRI Studies
Arthur Mensch (Inria Parietal) · Julien Mairal (Inria) · Danilo Bzdok (RWTH Aachen University) · Bertrand Thirion (INRIA) · Gael Varoquaux (Parietal Team, INRIA)

A KL-LUCB algorithm for Large-Scale Crowdsourcing
Ervin Tanczos (University of Wisconsin - Madison) · Robert Nowak (University of Wisconsion-Madison) · Bob Mankoff (Former Cartoon Editor of The New Yorker)

Collaborative Deep Learning in Fixed Topology Networks
Zhanhong Jiang (Iowa State University) · Aditya Balu (Iowa State University) · Chinmay Hegde (Iowa State University) · Soumik Sarkar (Iowa State University)

Fast-Slow Recurrent Neural Networks
Asier Mujika (ETH Zürich) · Florian Meier (ETH Zurich) · Angelika Steger (ETH Zurich)

Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Siddharth N (University of Oxford) · T. Brooks Paige (Alan Turing Institute) · Jan-Willem van de Meent (Northeastern University) · Alban Desmaison (Oxford University) · Frank Wood (University of Oxford) · Noah Goodman (Stanford University) · Pushmeet Kohli (Microsoft Research) · Philip Torr (University of Oxford)

Learning to Generalize Intrinsic Images with a Structured Disentangling Autoencoder
Michael Janner (MIT) · Jiajun Wu (MIT) · Tejas Kulkarni (DeepMind) · Ilker Yildirim (MIT) · Josh Tenenbaum (MIT)

Exploring Generalization in Deep Learning
Behnam Neyshabur (Institute for Advanced Study) · Srinadh Bhojanapalli (Toyota Technological Institute at Chicago) · Nati Srebro (TTI-Chicago)

A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control
Fanny Yang (University of California, Berkeley) · Aaditya Ramdas (University of California, Berkeley) · Kevin Jamieson (UC Berkeley) · Martin Wainwright (UC Berkeley)

Fader Networks: Generating Image Variations by Sliding Attribute Values
Guillaume Lample (Facebook AI Research) · Neil Zeghidour (Facebook A.I. Research / Ecole Normale Supérieure) · Nicolas Usunier (Facebook AI Research) · Antoine Bordes (Facebook AI Research) · Ludovic DENOYER (Universite Pierre et Marie Curie - Paris) · Marc'Aurelio Ranzato (Facebook)

Action Centered Contextual Bandits
Kristjan Greenewald (University of Michigan) · Ambuj Tewari (University of Michigan) · Susan Murphy (University of Michigan) · Predag Klasnja ()

Estimating Mutual Information for Discrete-Continuous Mixtures
Weihao Gao (UIUC) · Sreeram Kannan () · Sewoong Oh (UIUC) · Pramod Viswanath (UIUC)

Attention is All you Need
Ashish Vaswani (Google Brain) · Noam Shazeer (Google) · Niki Parmar (Google) · Llion Jones (Google) · Jakob Uszkoreit (Google, Inc.) · Aidan N Gomez (University of Toronto) · Łukasz Kaiser (Google Brain)

Recurrent Ladder Networks
Isabeau Prémont-Schwarz (The Curious Ai Company) · Alexander Ilin (The Curious AI company) · Tele Hao (The Curious AI Company) · Antti Rasmus () · Rinu Boney () · Harri Valpola (The Curious AI Company)

Parameter-Free Online Learning via Model Selection
Dylan J Foster (Cornell University) · Satyen Kale (Google) · Mehryar Mohri (Courant Institute and Google) · Karthik Sridharan (Cornell University)

Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction
Zhan Shi (University of Illinois at Chicago) · Xinhua Zhang (University of Illinois at Chicago) · Yaoliang Yu ()

Unbounded cache model for online language modeling with open vocabulary
Edouard Grave () · Moustapha Cisse (Facebook AI Research) · Armand Joulin (Facebook AI research)

Predictive State Recurrent Neural Networks
Carlton Downey (Carnegie Mellon University) · Ahmed Hefny (Carnegie Mellon University) · Byron Boots (Georgia Tech / Google Brain) · Geoffrey Gordon (CMU) · Boyue Li (Carnegie Mellon University)

Early stopping for kernel boosting algorithms: A general analysis with localized complexities
Yuting Wei (University of California, Berkeley) · Fanny Yang (University of California, Berkeley) · Martin Wainwright (UC Berkeley)

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement
Maithra Raghu (Cornell University and Google Brain) · Justin Gilmer (Google Brain) · Jason Yosinski (Uber) · Jascha Sohl-Dickstein (Google Brain)

Convolutional Phase Retrieval
Qing Qu (Columbia University) · Yuqian Zhang (Columbia University) · Yonina Eldar (Israel Institute of Technology) · John Wright (Columbia University)

Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma
Zhuoran Yang (Princeton University) · krishnakumar balasubramanian (georgia tech) · Princeton Zhaoran Wang (Princeton, Phd student) · Han Liu (Tencent AI Lab)

Gaussian Quadrature for Kernel Features
Tri Dao (Stanford University) · Christopher M De Sa (Stanford) · Christopher Ré (Stanford)

Value Prediction Network
Junhyuk Oh (University of Michigan) · Satinder Singh (University of Michigan) · Honglak Lee (Google / U. Michigan)

On Learning Errors of Structured Prediction with Approximate Inference
Yuanbin Wu (East China Normal University)

Efficient Second-Order Online Kernel Learning with Adaptive Embedding
Daniele Calandriello (INRIA Lille - Nord Europe) · Michal Valko (Inria Lille - Nord Europe) · Alessandro Lazaric (INRIA Lille-Nord Europe)

Implicit Regularization in Matrix Factorization
Suriya Gunasekar (TTI Chicago) · Blake Woodworth (Toyota Technological Institute at Chicago) · Srinadh Bhojanapalli (Toyota Technological Institute at Chicago) · Behnam Neyshabur (Institute for Advanced Study) · Nati Srebro (TTI-Chicago)

Optimal Shrinkage of Singular Values Under Random Data Contamination
Danny Barash (The Hebrew University Of Jerusalem) · Matan Gavish (Hebrew University)

Delayed Mirror Descent in Continuous Games
Zhengyuan Zhou (Stanford University) · Panayotis Mertikopoulos () · Nicholas Bambos () · Peter W Glynn (Stanford University) · Claire Tomlin (UC Berkeley)

Asynchronous Coordinate Descent under More Realistic Assumptions
Tao Sun (National university of defense technology) · Robert Hannah (UCLA) · Wotao Yin (University of California, Los Angeles)

Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls
Zeyuan Allen-Zhu (Microsoft Research) · Elad Hazan (Princeton University) · Wei Hu (Princeton University) · Yuanzhi Li (Princeton University)

Hierarchical Clustering Beyond the Worst-Case
Vincent Cohen-Addad (University of Copenhagen) · Varun Kanade (University of Oxford) · Frederik Mallmann-Trenn (ENS)

Invariance and Stability of Deep Convolutional Representations
Alberto Bietti (Inria) · Julien Mairal (Inria)

Statistical Cost Sharing
Eric Balkanski (Harvard University) · Umar Syed (Google Research) · Sergei Vassilvitskii (Google)

The Expressive Power of Neural Networks: A View from the Width
Zhou Lu (Peking University) · Hongming Pu (Peking university) · Feicheng Wang (Peking University) · Zhiqiang Hu (Peking University) · Liwei Wang (Peking University)

Spectrally-normalized margin bounds for neural networks
Matus Telgarsky (UIUC) · Peter Bartlett (UC Berkeley) · Dylan J Foster (Cornell University)

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Samuel Daulton (Harvard University) · Taylor Killian (Harvard University) · Finale Doshi-Velez (Harvard) · George Konidaris (Brown University)

Population Matching Discrepancy and Applications in Deep Learning
Jianfei Chen (Tsinghua University) · Chongxuan LI (Tsinghua University) · Yizhong Ru (Tsinghua University) · Jun Zhu (Tsinghua University)

Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Ga Wu (University of Toronto) · Buser Say (University of Toronto) · Scott Sanner (University of Toronto)

Boltzmann Exploration Done Right
Nicolò Cesa-Bianchi (Università degli Studi di Milano, Italy) · Claudio Gentile (University of Insubria) · Gergely Neu () · Gabor Lugosi (Pompeu Fabra University)

Towards the ImageNet-CNN of NLP: Pretraining Sentence Encoders with Machine Translation
Bryan McCann (Salesforce Research) · James Bradbury (Salesforce Research) · Caiming Xiong (Salesforce Research) · Richard Socher (MetaMind)

Neural Discrete Representation Learning
Aaron van den Oord (Google Deepmind) · Oriol Vinyals (Google DeepMind) · koray kavukcuoglu (DeepMind)

Generalizing GANs: A Turing Perspective
Roderich Gross (The University of Sheffield) · Yue Gu (The University of Sheffield) · Wei Li (University of York) · Melvin Gauci (Harvard University)

Scalable Log Determinants for Gaussian Process Kernel Learning
David Eriksson (Cornell University) · Kun Dong (Cornell University) · David Bindel (Cornell University) · Andrew Wilson (Cornell University) · Hannes Nickisch (Philips Research)

Poincaré Embeddings for Learning Hierarchical Representations
Maximillian Nickel (Facebook) · Douwe Kiela (Facebook AI Research)

Learning Combinatorial Optimization Algorithms over Graphs
Elias Khalil (Georgia Tech) · Hanjun Dai (Georgia Tech) · Yuyu Zhang () · Bistra Dilkina (Georgia Institute of Technology) · Le Song (Georgia Institute of Technology)

Robust Conditional Probabilities
Yoav Wald (Hebrew University) · Amir Globerson (HUJI)

Learning with Bandit Feedback in Potential Games
Amélie C Heliou (Univ. Grenoble Alpes) · Johanne Cohen (LRI-CNRS) · Panayotis Mertikopoulos ()

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe (McGill University) · YI WU (UC Berkeley) · Aviv Tamar (UC Berkeley) · Jean Harb (McGill University) · OpenAI Pieter Abbeel (OpenAI, UC Berkeley) · Igor Mordatch (OpenAI)

Communication-Efficient Distributed Learning of Discrete Distributions
Ilias Diakonikolas () · Elena Grigorescu (Purdue University) · Jerry Li (MIT) · Abhiram Natarajan (Purdue University) · Krzysztof Onak (IBM T.J. Watson Research Center) · Ludwig Schmidt (MIT)

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan (Google Deepmind) · Alexander Pritzel (Google Deepmind) · Charles Blundell (DeepMind)

When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness
Chris Russell (The Alan Turing Institute/ The University of Surrey) · Ricardo Silva (ucl.ac.uk) · Matt Kusner (Alan Turing Institute) · Joshua Loftus (The Alan Turing Institute)

Matrix Norm Estimation from a Few Entries
Sewoong Oh (UIUC) · Ashish Khetan (University of Illinois Urbana Champaign)

Deep Networks for Decoding Natural Images from Retinal Signals
Nikhil Parthasarathy (New York University) · Eleanor Batty (Columbia University) · William Falcon (Columbia University) · Thomas Rutten (Columbia University) · Mohit Rajpal (Columbia University) · E.J. Chichilnisky (Stanford University) · Liam Paninski (Columbia University)

Causal Effect Inference with Deep Latent Variable Models
Christos Louizos (University of Amsterdam) · Uri Shalit () · Joris M Mooij (University of Amsterdam) · David Sontag (MIT) · Richard Zemel (University of Toronto) · Max Welling (University of Amsterdam and University of California Irvine and CIFAR)

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Asish Ghoshal (Purdue University) · Jean Honorio (Purdue University)

Gradient Episodic Memory for Continuum Learning
David Lopez-Paz (Facebook AI Research) · Marc'Aurelio Ranzato (Facebook)

Radon Machines: Effective Parallelisation for Machine Learning
Michael Kamp (Fraunhofer IAIS) · Mario Boley (Max Planck Institute for Informatics and Saarland University) · Olana Missura (Google Inc) · Thomas Gärtner (University of Nottingham)

Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding
Arya Mazumdar (University of Massachusetts Amherst) · Soumyabrata Pal (University of Massachusetts Amherst)

Clustering Stable Instances of Euclidean k-means.
Aravindan Vijayaraghavan (Northwestern University) · Abhratanu Dutta (Northwestern University) · Alex Wang (Northwestern University)

Good Semi-supervised Learning That Requires a Bad GAN
Zihang Dai (Carnegie Mellon University) · Zhilin Yang (Carnegie Mellon University) · Fan Yang (Carnegie Mellon University) · William W Cohen (Carnegie Mellon University) · Ruslan Salakhutdinov ()

On Blackbox Backpropagation and Jacobian Sensing
Krzysztof Choromanski () · Vikas Sindhwani ()

Protein Interface Prediction using Graph Convolutional Networks
Alex Fout (Colorado State University) · Basir Shariat (Colorado State University) · Jonathon Byrd (Colorado State University) · Asa Ben-Hur (Colorado State University)

Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities
Michael Eickenberg (UC Berkeley) · Georgios Exarchakis (Ecole Normale Supérieure) · Matthew Hirn (Michigan State University) · Stephane Mallat (Ecole normale superieure)

Towards Generalization and Simplicity in Continuous Control
Aravind Rajeswaran (University of Washington) · Kendall Lowrey (University of Washington) · Emanuel Todorov (University of Washington) · Sham Kakade (University of Washington)

Random Projection Filter Bank for Time Series Data
Amir-massoud Farahmand (MERL) · Sepideh Pourazarm (MERL) · Daniel Nikovski ()

Filtering Variational Objectives
Chris Maddison (Oxford) · John Lawson (Google Brain) · George Tucker (Google Brain) · Mohammad Norouzi () · Nicolas Heess (Google DeepMind) · Andriy Mnih () · Yee Teh (DeepMind) · Arnaud Doucet (Oxford)

On Frank-Wolfe and Equilibrium Computation
Jacob D Abernethy (University of Michigan) · Jun-Kun Wang (Georgia Institute of Technology)

Modulating early visual processing by language
Harm de Vries (Université de Montréal) · Florian Strub (University of Lille) · Jeremie Mary (INRIA / Univ. Lille) · Hugo Larochelle (Google Brain) · Olivier Pietquin (DeepMind) · Aaron C Courville (U. Montreal)

Learning Mixture of Gaussians with Streaming Data
Aditi Raghunathan (Stanford University) · Prateek Jain (Microsoft Research) · Ravishankar Krishnawamy (Microsoft Research India)

Practical Hash Functions for Similarity Estimation and Dimensionality Reduction
Søren Dahlgaard (University of Copenhagen) · Mathias Knudsen (University of Copenhagen) · Mikkel Thorup (University of Copenhagen)

Two Time-Scale Update Rule for Generative Adversarial Nets
Hubert Ramsauer (LIT AI Lab / University Linz) · Martin Heusel (LIT AI Lab / University Linz) · Sepp Hochreiter (LIT AI Lab / University Linz) · Bernhard Nessler (Johannes Kepler University Linz) · Thomas Unterthiner (LIT AI Lab / University Linz)

The Scaling Limit of High-Dimensional Online Independent Component Analysis
Chuang Wang (Harvard University) · Yue Lu (Harvard University)

Approximation Algorithms for ${\ell }_0$-Low Rank Approximation
Pavel Kolev (Max-Planck-Institut für Informatik) · Karl Bringmann (Saarland University) · David Woodruff ()

The power of absolute discounting: all-dimensional distribution estimation
Moein Falahatgar (UCSD) · Mesrob Ohannessian (Toyota Technological Institute at Chicago) · Alon Orlitsky (University of California, San Diego) · Venkatadheeraj Pichapati (UCSD)

Supervised Adversarial Domain Adaptation
Saeid Motiian (West Virginia University) · Quinn Jones (West Virginia University) · Gianfranco Doretto (West Virginia University)

Spectral Mixture Kernels for Multi-Output Gaussian Processes
Gabriel Parra (Universidad de Chile) · Felipe Tobar (Universidad de Chile)

Neural Expectation Maximization
Klaus Greff (IDSIA) · Sjoerd van Steenkiste (The Swiss AI Lab - IDSIA) · Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI) - NNAISENSE)

Online Learning of Linear Dynamical Systems
Elad Hazan (Princeton University) · Karan Singh (Princeton University) · Cyril Zhang (Princeton University)

Z-Forcing: Training Stochastic Recurrent Networks
Marc-Alexandre Côté (Microsoft Maluuba) · Alessandro Sordoni (Microsoft Maluuba) · Anirudh Goyal ALIAS PARTH GOYAL (Université de Montréal) · Nan Ke (MILA, École Polytechnique de Montréal) · Yoshua Bengio (U. Montreal)

Thalamus Gated Recurrent Modules
Danijar Hafner (University College London) · Alexander Irpan (Google) · James Davidson (Google Brain) · Nicolas Heess (Google DeepMind)

Neural Variational Inference and Learning in Undirected Graphical Models
Volodymyr Kuleshov (Stanford University) · Stefano Ermon (Stanford)

Subspace Clustering via Tangent Cones
Amin Jalali (Wisconsin Institute for Discovery) · Rebecca Willett (University of Wisconsin)

The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Hongyuan Mei (JOHNS HOPKINS UNIVERSITY) · Jason Eisner (Johns Hopkins University)

Inverse Reward Design
Dylan Hadfield-Menell (UC Berkeley) · Smitha Milli (UC Berkeley) · Stuart J Russell (UC Berkeley) · Pieter Abbeel (OpenAI / UC Berkeley / Gradescope) · Anca Dragan (UC Berkeley)

Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Kirill Neklyudov (Yandex) · Dmitry Molchanov (Skolkovo Institute of Science and Technology) · Arsenii Ashukha (HSE, Yandex) · Dmitry Vetrov (Higher School of Economics, Yandex)

Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Ritambhara Singh (University of Virginia) · Jack Lanchantin (University of Virginia) · Yanjun Qi (University of Virginia)

Acceleration and Averaging in Stochastic Descent Dynamics
Walid Krichene (Google)

Kernel functions based on triplet comparisons
Matthäus Kleindessner (University of Tübingen) · Ulrike von Luxburg (University of Tübingen)

An Error Detection and Correction Framework for Connectomics
Jonathan Zung (Princeton University) · Ignacio Tartavull (Princeton Universitiy)

Style Transfer from Non-parallel Text by Cross-Alignment
Tianxiao Shen (MIT) · Tao Lei (MIT) · Regina Barzilay (Massachusetts Institute of Technology) · Tommi Jaakkola (MIT)

Cross-Spectral Factor Analysis
Neil Gallagher (Duke University) · Kyle Ulrich () · Austin Talbot (Duke University) · Kafui Dzirasa (Duke University) · David E Carlson (Duke University) · Lawrence Carin (Duke University)

Stochastic Submodular Maximization: The Case of Coverage Functions
Mohammad Karimi (ETH Zurich) · Mario Lucic (Google Brain (Zurich)) · Hamed Hassani (UPenn) · Andreas Krause (ETHZ)

On Distributed Hierarchical Clustering
Mahsa Derakhshan (University of Maryland) · Soheil Behnezhad (University of Maryland) · Mohammadhossein Bateni (Google research) · Vahab Mirrokni (Google Research NYC) · MohammadTaghi Hajiaghayi (University of Maryland) · Silvio Lattanzi (Google Research) · Raimondas Kiveris (Google research)

Unsupervised Transformation Learning via Convex Relaxations
Tatsunori B Hashimoto (Massachusetts Institute of Technology) · Percy Liang (Stanford University) · John C Duchi (Stanford)

A Sharp Error Analysis for the Fused Lasso, with Implications to Broader Settings and Approximate Screening
Kevin Lin (Carnegie Mellon University) · James Sharpnack () · Alessandro Rinaldo (CMU) · Ryan Tibshirani (Carnegie Mellon University)

Efficient Computation of Moments in Sum-Product Networks
Han Zhao (Carnegie Mellon University)

A Meta-Learning Perspective on Cold-Start Recommendations for Items
Manasi Vartak (Massachusetts Institute of Technology) · Hugo Larochelle (Google Brain) · Arvind Thiagarajan (Twitter)

Predicting Scene Parsing and Motion Dynamics in the Future
Xiaojie Jin (National University of Singapore) · Jiashi Feng (National University of Singapore) · Huaxin Xiao (NUDT) · Yunpeng Chen (National University of Singapore) · Shuicheng Yan (Qihoo 360 AI Institute) · Xiaohui Shen (Adobe) · Jimei Yang (Adobe Research) · Zequn Jie (Tencent AI Lab) · Li Ping (Hangzhou Dianzi University, National University of Singapore)

Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Geoffrey Roeder (University of Toronto) · Yuhuai Wu (University of Toronto) · David Duvenaud (University of Toronto)

Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification
Muhammad Farhan (LUMS) · Juvaria Tariq (Emory Univeristy) · Arif Zaman (LUMS) · Mudassir Shabbir (ITU) · Imdad Ullah Khan (LUMS)

Kernel Feature Selection via Conditional Covariance Minimization
Jianbo Chen (University of California, Berkeley) · Mitchell Stern (UC Berkeley) · Martin J Wainwright (UC Berkeley) · Michael Jordan (UC Berkeley)

Statistical Convergence Analysis of Gradient EM on General Gaussian Mixture Models
Bowei Yan (University of Texas at Austin) · Mingzhang Yin (University of Texas at Austin) · Purnamrita Sarkar (UT Austin)

Real Time Image Saliency for Black Box Classifiers
Piotr Dabkowski (Cambridge University) · Yarin Gal (University of Oxford)

Houdini: Democratizing Adversarial Examples
Moustapha Cisse (Facebook AI Research) · Yossi Adi (Bar Ilan University) · Natalia Neverova (Facebook AI Research) · Joseph Keshet (Bar-Ilan University)

Efficient and Flexible Inference for Stochastic Systems
Stefan Bauer (ETH Zürich) · Djordje Miladinovic (ETH Zurich) · Nico S Gorbach (Swiss Federal Institute of Technology Zurich (ETHZ)) · Joachim M Buhmann (ETH Zurich)

When Cyclic Coordinate Descent Beats Randomized Coordinate Descent
Mert Gurbuzbalaban (Rutgers University) · Nuri Vanli (Massachusetts Institute of Technology) · Asuman Ozdaglar (Massachusetts Institute of Technology)

Active Learning from Peers
Keerthiram Murugesan (Carnegie Mellon University) · Jaime Carbonell (CMU)

Learning Causal Graphs with Latent Variables
Murat Kocaoglu (University of Texas at Austin) · Karthikeyan Shanmugam (IBM Research, NY) · Elias Bareinboim ()

Learning to Model the Tail
Yu-Xiong Wang (Carnegie Mellon University) · Deva Ramanan (Carnegie Mellon University) · Martial Hebert (cmu)

Stochastic Mirror Descent for Non-Convex Optimization
Zhengyuan Zhou (Stanford University) · Panayotis Mertikopoulos () · Nicholas Bambos () · Stephen Boyd (Stanford University) · Peter W Glynn (Stanford University)

On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models
Adarsh Prasad (Carnegie Mellon University) · Pradeep Ravikumar (Carnegie Mellon University)

Maxing and Ranking with Few Assumptions
Venkatadheeraj Pichapati (UC San Diego) · Alon Orlitsky (University of California, San Diego) · Vaishakh Ravindrakumar (UC San Diego) · Moein Falahatgar () · Yi Hao ()

On clustering network-valued data
Soumendu Sundar Mukherjee (University of California, Berkeley) · Purnamrita Sarkar (UT Austin) · Lizhen Lin (The University of Texas at Austin)

A General Framework for Robust Interactive Learning
Ehsan Emamjomeh-Zadeh (U. of Southern California) · David Kempe (U. of Southern California)

Multi-view Matrix Factorization for Linear Dynamical System Estimation
Mahdi Karami (University of Alberta) · Martha White () · Dale Schuurmans (Google) · Csaba Szepesvari (University of Alberta)