NeurIPS 2017
679 papers
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, Pieter Abbeel A Bayesian Data Augmentation Approach for Learning Deep Models
Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, Ian Reid A Decomposition of Forecast Error in Prediction Markets
Miro Dudik, Sebastien Lahaie, Ryan M Rogers, Jennifer Wortman Vaughan A Graph-Theoretic Approach to Multitasking
Noga Alon, Daniel Reichman, Igor Shinkar, Tal Wagner, Sebastian Musslick, Jonathan D. Cohen, Tom Griffiths, Biswadip Dey, Kayhan Ozcimder A Linear-Time Kernel Goodness-of-Fit Test
Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton A Meta-Learning Perspective on Cold-Start Recommendations for Items
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle A Multi-Agent Reinforcement Learning Model of Common-Pool Resource Appropriation
Julien Pérolat, Joel Z. Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel A New Theory for Matrix Completion
Guangcan Liu, Qingshan Liu, Xiaotong Yuan A Simple Neural Network Module for Relational Reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel A-NICE-MC: Adversarial Training for MCMC
Jiaming Song, Shengjia Zhao, Stefano Ermon Action Centered Contextual Bandits
Kristjan Greenewald, Ambuj Tewari, Susan Murphy, Predag Klasnja Active Learning from Peers
Keerthiram Murugesan, Jaime Carbonell AdaGAN: Boosting Generative Models
Ilya O Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf Adaptive Batch Size for Safe Policy Gradients
Matteo Papini, Matteo Pirotta, Marcello Restelli Adaptive Bayesian Sampling with Monte Carlo EM
Anirban Roychowdhury, Srinivasan Parthasarathy Adaptive Stimulus Selection for Optimizing Neural Population Responses
Benjamin Cowley, Ryan Williamson, Katerina Clemens, Matthew Smith, Byron M. Yu Adversarial Ranking for Language Generation
Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, Ming-ting Sun Adversarial Surrogate Losses for Ordinal Regression
Rizal Fathony, Mohammad Ali Bashiri, Brian Ziebart Adversarial Symmetric Variational Autoencoder
Yuchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin Affinity Clustering: Hierarchical Clustering at Scale
Mohammadhossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Raimondas Kiveris, Silvio Lattanzi, Vahab Mirrokni ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
Chunyuan Li, Hao Liu, Changyou Chen, Yuchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin An Empirical Study on the Properties of Random Bases for Kernel Methods
Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Fei Sha An Error Detection and Correction Framework for Connectomics
Jonathan Zung, Ignacio Tartavull, Kisuk Lee, H. Sebastian Seung Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin Avoiding Discrimination Through Causal Reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf Balancing Information Exposure in Social Networks
Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti Bayesian Compression for Deep Learning
Christos Louizos, Karen Ullrich, Max Welling Bayesian GAN
Yunus Saatci, Andrew G Wilson Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew G Wilson, Peter Frazier Best Response Regression
Omer Ben-Porat, Moshe Tennenholtz Boltzmann Exploration Done Right
Nicolò Cesa-Bianchi, Claudio Gentile, Gabor Lugosi, Gergely Neu Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling Certified Defenses for Data Poisoning Attacks
Jacob Steinhardt, Pang Wei W Koh, Percy Liang Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet Clustering Billions of Reads for DNA Data Storage
Cyrus Rashtchian, Konstantin Makarychev, Miklos Racz, Siena Ang, Djordje Jevdjic, Sergey Yekhanin, Luis Ceze, Karin Strauss Clustering Stable Instances of Euclidean K-Means.
Aravindan Vijayaraghavan, Abhratanu Dutta, Alex Wang Collaborative Deep Learning in Fixed Topology Networks
Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar Collaborative PAC Learning
Avrim Blum, Nika Haghtalab, Ariel D Procaccia, Mingda Qiao Collecting Telemetry Data Privately
Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin Communication-Efficient Distributed Learning of Discrete Distributions
Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt Compatible Reward Inverse Reinforcement Learning
Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli Concrete Dropout
Yarin Gal, Jiri Hron, Alex Kendall Conservative Contextual Linear Bandits
Abbas Kazerouni, Mohammad Ghavamzadeh, Yasin Abbasi Yadkori, Benjamin Van Roy Consistent Multitask Learning with Nonlinear Output Relations
Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil Consistent Robust Regression
Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar Context Selection for Embedding Models
Liping Liu, Francisco Ruiz, Susan Athey, David Blei Continual Learning with Deep Generative Replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon Kim Controllable Invariance Through Adversarial Feature Learning
Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig Convolutional Gaussian Processes
Mark van der Wilk, Carl Edward Rasmussen, James Hensman Convolutional Phase Retrieval
Qing Qu, Yuqian Zhang, Yonina Eldar, John Wright Cortical Microcircuits as Gated-Recurrent Neural Networks
Rui Costa, Ioannis Alexandros Assael, Brendan Shillingford, Nando de Freitas, TIm Vogels Cost Efficient Gradient Boosting
Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler Counterfactual Fairness
Matt J Kusner, Joshua Loftus, Chris Russell, Ricardo Silva Countering Feedback Delays in Multi-Agent Learning
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter W. Glynn, Claire Tomlin Cross-Spectral Factor Analysis
Neil Gallagher, Kyle R Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E Carlson Decoding with Value Networks for Neural Machine Translation
Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tie-Yan Liu Deconvolutional Paragraph Representation Learning
Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, Lawrence Carin Deep Hyperalignment
Muhammad Yousefnezhad, Daoqiang Zhang Deep Hyperspherical Learning
Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song Deep Lattice Networks and Partial Monotonic Functions
Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta Deep Learning for Precipitation Nowcasting: A Benchmark and a New Model
Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun Woo Deep Learning with Topological Signatures
Christoph Hofer, Roland Kwitt, Marc Niethammer, Andreas Uhl Deep Mean-Shift Priors for Image Restoration
Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin Deep Reinforcement Learning from Human Preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, Dario Amodei Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander J Smola Deep Subspace Clustering Networks
Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, Ian Reid Deep Supervised Discrete Hashing
Qi Li, Zhenan Sun, Ran He, Tieniu Tan Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Andrew Gibiansky, Sercan Arik, Gregory Diamos, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou Deliberation Networks: Sequence Generation Beyond One-Pass Decoding
Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu, Tie-Yan Liu Differentially Private Bayesian Learning on Distributed Data
Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela Dilated Recurrent Neural Networks
Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael Witbrock, Mark A Hasegawa-Johnson, Thomas S. Huang Discovering Potential Correlations via Hypercontractivity
Hyeji Kim, Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath Distral: Robust Multitask Reinforcement Learning
Yee Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu DPSCREEN: Dynamic Personalized Screening
Kartik Ahuja, William Zame, Mihaela van der Schaar Dual Path Networks
Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, Jiashi Feng Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis
Jian Zhao, Lin Xiong, Panasonic Karlekar Jayashree, Jianshu Li, Fang Zhao, Zhecan Wang, Panasonic Sugiri Pranata, Panasonic Shengmei Shen, Shuicheng Yan, Jiashi Feng Dualing GANs
Yujia Li, Alexander Schwing, Kuan-Chieh Wang, Richard Zemel Dynamic Revenue Sharing
Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, IIIS Song Zuo Dynamic Routing Between Capsules
Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms
Yogatheesan Varatharajah, Min Jin Chong, Krishnakant Saboo, Brent Berry, Benjamin Brinkmann, Gregory Worrell, Ravishankar Iyer Effective Parallelisation for Machine Learning
Michael Kamp, Mario Boley, Olana Missura, Thomas Gärtner Efficient and Flexible Inference for Stochastic Systems
Stefan Bauer, Nico S Gorbach, Djordje Miladinovic, Joachim M Buhmann Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation
Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi Eigen-Distortions of Hierarchical Representations
Alexander Berardino, Valero Laparra, Johannes Ballé, Eero Simoncelli End-to-End Differentiable Proving
Tim Rocktäschel, Sebastian Riedel Ensemble Sampling
Xiuyuan Lu, Benjamin Van Roy Exploring Generalization in Deep Learning
Behnam Neyshabur, Srinadh Bhojanapalli, David Mcallester, Nati Srebro F-GANs in an Information Geometric Nutshell
Richard Nock, Zac Cranko, Aditya K Menon, Lizhen Qu, Robert C. Williamson Fader Networks:Manipulating Images by Sliding Attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato Fair Clustering Through Fairlets
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii FALKON: An Optimal Large Scale Kernel Method
Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco Fast-Slow Recurrent Neural Networks
Asier Mujika, Florian Meier, Angelika Steger Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet S Talwalkar Few-Shot Adversarial Domain Adaptation
Saeid Motiian, Quinn Jones, Seyed Iranmanesh, Gianfranco Doretto Filtering Variational Objectives
Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Teh Fisher GAN
Youssef Mroueh, Tom Sercu Flexible Statistical Inference for Mechanistic Models of Neural Dynamics
Jan-Matthis Lueckmann, Pedro J Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H Macke Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Urs Köster, Tristan Webb, Xin Wang, Marcel Nassar, Arjun K Bansal, William Constable, Oguz Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J Pai, Naveen Rao From Parity to Preference-Based Notions of Fairness in Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Rodriguez, Krishna Gummadi, Adrian Weller From Which World Is Your Graph
Cheng Li, Felix MF Wong, Zhenming Liu, Varun Kanade GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter Gauging Variational Inference
Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin Gaussian Quadrature for Kernel Features
Tri Dao, Christopher M De Sa, Christopher Ré Generalizing GANs: A Turing Perspective
Roderich Gross, Yue Gu, Wei Li, Melvin Gauci Generative Local Metric Learning for Kernel Regression
Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank Park, Daniel D Lee GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Alex M Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio Good Semi-Supervised Learning That Requires a Bad GAN
Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, Ruslan Salakhutdinov GP CaKe: Effective Brain Connectivity with Causal Kernels
Luca Ambrogioni, Max Hinne, Marcel Van Gerven, Eric Maris Gradient Descent Can Take Exponential Time to Escape Saddle Points
Simon S Du, Chi Jin, Jason Lee, Michael I Jordan, Aarti Singh, Barnabas Poczos Gradient Methods for Submodular Maximization
Hamed Hassani, Mahdi Soltanolkotabi, Amin Karbasi Graph Matching via Multiplicative Update Algorithm
Bo Jiang, Jin Tang, Chris Ding, Yihong Gong, Bin Luo Group Sparse Additive Machine
Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang Hierarchical Attentive Recurrent Tracking
Adam Kosiorek, Alex Bewley, Ingmar Posner Hierarchical Clustering Beyond the Worst-Case
Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn Hierarchical Methods of Moments
Matteo Ruffini, Guillaume Rabusseau, Borja Balle Hindsight Experience Replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, Wojciech Zaremba Hybrid Reward Architecture for Reinforcement Learning
Harm Van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, Jeffrey Tsang Hypothesis Transfer Learning via Transformation Functions
Simon S Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos Identification of Gaussian Process State Space Models
Stefanos Eleftheriadis, Tom Nicholson, Marc Deisenroth, James Hensman Imagination-Augmented Agents for Deep Reinforcement Learning
Sébastien Racanière, Theophane Weber, David Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra Implicit Regularization in Matrix Factorization
Suriya Gunasekar, Blake E Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro Improved Dynamic Regret for Non-Degenerate Functions
Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou Improved Training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron C. Courville Inferring Generative Model Structure with Static Analysis
Paroma Varma, Bryan D He, Payal Bajaj, Nishith Khandwala, Imon Banerjee, Daniel Rubin, Christopher Ré Influence Maximization with $\varepsilon$-Almost Submodular Threshold Functions
Qiang Li, Wei Chen, Institute of Computing Xiaoming Sun, Institute of Computing Jialin Zhang Integration Methods and Optimization Algorithms
Damien Scieur, Vincent Roulet, Francis Bach, Alexandre d'Aspremont Interactive Submodular Bandit
Lin Chen, Andreas Krause, Amin Karbasi Inverse Filtering for Hidden Markov Models
Robert Mattila, Cristian Rojas, Vikram Krishnamurthy, Bo Wahlberg Inverse Reward Design
Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart Russell, Anca Dragan Is the Bellman Residual a Bad Proxy?
Matthieu Geist, Bilal Piot, Olivier Pietquin Joint Distribution Optimal Transportation for Domain Adaptation
Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy K-Medoids for K-Means Seeding
James Newling, François Fleuret Kernel Feature Selection via Conditional Covariance Minimization
Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I Jordan Label Distribution Learning Forests
Wei Shen, Kai Zhao, Yilu Guo, Alan L. Yuille Learned in Translation: Contextualized Word Vectors
Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher Learning a Multi-View Stereo Machine
Abhishek Kar, Christian Häne, Jitendra Malik Learning Active Learning from Data
Ksenia Konyushkova, Raphael Sznitman, Pascal Fua Learning Affinity via Spatial Propagation Networks
Sifei Liu, Shalini De Mello, Jinwei Gu, Guangyu Zhong, Ming-Hsuan Yang, Jan Kautz Learning Causal Structures Using Regression Invariance
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang Learning Combinatorial Optimization Algorithms over Graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, Le Song Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Siddharth N, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah Goodman, Pushmeet Kohli, Frank Wood, Philip Torr Learning from Complementary Labels
Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama Learning Low-Dimensional Metrics
Blake Mason, Lalit Jain, Robert Nowak Learning Mixture of Gaussians with Streaming Data
Aditi Raghunathan, Prateek Jain, Ravishankar Krishnawamy Learning Neural Representations of Human Cognition Across Many fMRI Studies
Arthur Mensch, Julien Mairal, Danilo Bzdok, Bertrand Thirion, Gael Varoquaux Learning Overcomplete HMMs
Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant Learning Populations of Parameters
Kevin Tian, Weihao Kong, Gregory Valiant Learning to Compose Domain-Specific Transformations for Data Augmentation
Alexander J Ratner, Henry Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré Learning to Inpaint for Image Compression
Mohammad Haris Baig, Vladlen Koltun, Lorenzo Torresani Learning to Model the Tail
Yu-Xiong Wang, Deva Ramanan, Martial Hebert Learning to Pivot with Adversarial Networks
Gilles Louppe, Michael Kagan, Kyle Cranmer Learning to See Physics via Visual De-Animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum Learning with Average Top-K Loss
Yanbo Fan, Siwei Lyu, Yiming Ying, Baogang Hu Learning with Bandit Feedback in Potential Games
Amélie Heliou, Johanne Cohen, Panayotis Mertikopoulos Learning with Feature Evolvable Streams
Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu Linearly Constrained Gaussian Processes
Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B Schön Local Aggregative Games
Vikas Garg, Tommi Jaakkola MarrNet: 3D Shape Reconstruction via 2.5d Sketches
Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, Josh Tenenbaum Maximum Margin Interval Trees
Alexandre Drouin, Toby Hocking, Francois Laviolette Maxing and Ranking with Few Assumptions
Moein Falahatgar, Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar Min-Max Propagation
Christopher Srinivasa, Inmar Givoni, Siamak Ravanbakhsh, Brendan J. Frey Minimal Exploration in Structured Stochastic Bandits
Richard Combes, Stefan Magureanu, Alexandre Proutiere Mixture-Rank Matrix Approximation for Collaborative Filtering
Dongsheng Li, Chao Chen, Wei Liu, Tun Lu, Ning Gu, Stephen Chu MMD GAN: Towards Deeper Understanding of Moment Matching Network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabas Poczos Model-Based Bayesian Inference of Neural Activity and Connectivity from All-Optical Interrogation of a Neural Circuit
Laurence Aitchison, Lloyd Russell, Adam M Packer, Jinyao Yan, Philippe Castonguay, Michael Hausser, Srinivas C. Turaga Model-Powered Conditional Independence Test
Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G Dimakis, Sanjay Shakkottai Modulating Early Visual Processing by Language
Harm de Vries, Florian Strub, Jeremie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C. Courville Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, Igor Mordatch Multi-Information Source Optimization
Matthias Poloczek, Jialei Wang, Peter Frazier Multi-Output Polynomial Networks and Factorization Machines
Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda Multi-Task Learning for Contextual Bandits
Aniket Anand Deshmukh, Urun Dogan, Clay Scott Multi-View Decision Processes: The Helper-AI Problem
Christos Dimitrakakis, David C. Parkes, Goran Radanovic, Paul Tylkin Multiscale Quantization for Fast Similarity Search
Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N Holtmann-Rice, David Simcha, Felix Yu Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
Daniel Milstein, Jason Pacheco, Leigh Hochberg, John D Simeral, Beata Jarosiewicz, Erik Sudderth Multitask Spectral Learning of Weighted Automata
Guillaume Rabusseau, Borja Balle, Joelle Pineau Natural Value Approximators: Learning When to Trust past Estimates
Zhongwen Xu, Joseph Modayil, Hado P van Hasselt, Andre Barreto, David Silver, Tom Schaul Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
Sanjiban Choudhury, Shervin Javdani, Siddhartha Srinivasa, Sebastian Scherer Neural Discrete Representation Learning
Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu Neural Expectation Maximization
Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons
Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E. J. Chichilnisky, Liam Paninski Neural Program Meta-Induction
Jacob Devlin, Rudy R Bunel, Rishabh Singh, Matthew Hausknecht, Pushmeet Kohli Noise-Tolerant Interactive Learning Using Pairwise Comparisons
Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, Artur Dubrawski Non-Convex Finite-Sum Optimization via SCSG Methods
Lihua Lei, Cheng Ju, Jianbo Chen, Michael I Jordan Non-Stationary Spectral Kernels
Sami Remes, Markus Heinonen, Samuel Kaski Nonlinear Acceleration of Stochastic Algorithms
Damien Scieur, Francis Bach, Alexandre d'Aspremont Off-Policy Evaluation for Slate Recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, Imed Zitouni On Clustering Network-Valued Data
Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin On Fairness and Calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, Kilian Q. Weinberger On Optimal Generalizability in Parametric Learning
Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, Vahid Tarokh On the Complexity of Learning Neural Networks
Le Song, Santosh Vempala, John Wilmes, Bo Xie OnACID: Online Analysis of Calcium Imaging Data in Real Time
Andrea Giovannucci, Johannes Friedrich, Matt Kaufman, Anne Churchland, Dmitri Chklovskii, Liam Paninski, Eftychios A Pnevmatikakis One-Shot Imitation Learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba Online Control of the False Discovery Rate with Decaying Memory
Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I Jordan Online Dynamic Programming
Holakou Rahmanian, Manfred K. Warmuth Online Learning for Multivariate Hawkes Processes
Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash Online Learning with a Hint
Ofer Dekel, Arthur Flajolet, Nika Haghtalab, Patrick Jaillet Online Multiclass Boosting
Young Hun Jung, Jack Goetz, Ambuj Tewari Optimized Pre-Processing for Discrimination Prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, Kush R Varshney Overcoming Catastrophic Forgetting by Incremental Moment Matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, Byoung-Tak Zhang Parallel Streaming Wasserstein Barycenters
Matthew Staib, Sebastian Claici, Justin M Solomon, Stefanie Jegelka Parameter-Free Online Learning via Model Selection
Dylan J Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan Parametric Simplex Method for Sparse Learning
Haotian Pang, Han Liu, Robert J Vanderbei, Tuo Zhao Perturbative Black Box Variational Inference
Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt PixelGAN Autoencoders
Alireza Makhzani, Brendan J. Frey Pose Guided Person Image Generation
Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo, Gang Niu, Marthinus C du Plessis, Masashi Sugiyama Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta Predicting Scene Parsing and Motion Dynamics in the Future
Xiaojie Jin, Huaxin Xiao, Xiaohui Shen, Jimei Yang, Zhe Lin, Yunpeng Chen, Zequn Jie, Jiashi Feng, Shuicheng Yan Predictive State Recurrent Neural Networks
Carlton Downey, Ahmed Hefny, Byron Boots, Geoffrey J. Gordon, Boyue Li Predictive-State Decoders: Encoding the Future into Recurrent Networks
Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris Kitani, J. Bagnell Process-Constrained Batch Bayesian Optimisation
Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin, Alessandra Sutti, Thomas Dorin, Murray Height, Paul Sanders, Svetha Venkatesh Quantifying How Much Sensory Information in a Neural Code Is Relevant for Behavior
Giuseppe Pica, Eugenio Piasini, Houman Safaai, Caroline Runyan, Christopher Harvey, Mathew Diamond, Christoph Kayser, Tommaso Fellin, Stefano Panzeri Question Asking as Program Generation
Anselm Rothe, Brenden M Lake, Todd Gureckis Random Permutation Online Isotonic Regression
Wojciech Kotlowski, Wouter M. Koolen, Alan Malek Random Projection Filter Bank for Time Series Data
Amir-massoud Farahmand, Sepideh Pourazarm, Daniel Nikovski Reconstruct & Crush Network
Erinc Merdivan, Mohammad Reza Loghmani, Matthieu Geist Reconstructing Perceived Faces from Brain Activations with Deep Adversarial Neural Decoding
Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob van Lier, Marcel A. J. van Gerven Recurrent Ladder Networks
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Ziyu Wang, Josh S Merel, Scott E Reed, Nando de Freitas, Gregory Wayne, Nicolas Heess Robust Optimization for Non-Convex Objectives
Robert S. Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis Rotting Bandits
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Ji Lin, Yongming Rao, Jiwen Lu, Jie Zhou Safe Adaptive Importance Sampling
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Phillip A Jang, Andrew Loeb, Matthew Davidow, Andrew G Wilson Scalable Log Determinants for Gaussian Process Kernel Learning
Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew G Wilson Scalable Model Selection for Belief Networks
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Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter Self-Supervised Intrinsic Image Decomposition
Michael Janner, Jiajun Wu, Tejas D Kulkarni, Ilker Yildirim, Josh Tenenbaum Self-Supervised Learning of Motion Capture
Hsiao-Yu Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki Shallow Updates for Deep Reinforcement Learning
Nir Levine, Tom Zahavy, Daniel J Mankowitz, Aviv Tamar, Shie Mannor Shape and Material from Sound
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Vincent Roulet, Alexandre d'Aspremont Sobolev Training for Neural Networks
Wojciech M. Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Swirszcz, Razvan Pascanu Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc V. Gool Solving Most Systems of Random Quadratic Equations
Gang Wang, Georgios Giannakis, Yousef Saad, Jie Chen Sparse Approximate Conic Hulls
Greg Van Buskirk, Benjamin Raichel, Nicholas Ruozzi Sparse Convolutional Coding for Neuronal Assembly Detection
Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht Sparse Embedded $k$-Means Clustering
Weiwei Liu, Xiaobo Shen, Ivor Tsang State Aware Imitation Learning
Yannick Schroecker, Charles L Isbell Statistical Cost Sharing
Eric Balkanski, Umar Syed, Sergei Vassilvitskii Stochastic Approximation for Canonical Correlation Analysis
Raman Arora, Teodor Vanislavov Marinov, Poorya Mianjy, Nati Srebro Stochastic Mirror Descent in Variationally Coherent Optimization Problems
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen Boyd, Peter W. Glynn Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach
Slobodan Mitrovic, Ilija Bogunovic, Ashkan Norouzi-Fard, Jakub M Tarnawski, Volkan Cevher Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P Vetrov Structured Embedding Models for Grouped Data
Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei Structured Generative Adversarial Networks
Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P Xing Style Transfer from Non-Parallel Text by Cross-Alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi Jaakkola Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues
Noga Alon, Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran, Amir Yehudayoff Subset Selection Under Noise
Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou Successor Features for Transfer in Reinforcement Learning
Andre Barreto, Will Dabney, Remi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, David Silver Targeting EEG/LFP Synchrony with Neural Nets
Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E Carlson Tensor Biclustering
Soheil Feizi, Hamid Javadi, David Tse The Marginal Value of Adaptive Gradient Methods in Machine Learning
Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, Benjamin Recht The Numerics of GANs
Lars Mescheder, Sebastian Nowozin, Andreas Geiger The Power of Absolute Discounting: All-Dimensional Distribution Estimation
Moein Falahatgar, Mesrob I Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati Toward Multimodal Image-to-Image Translation
Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A Efros, Oliver Wang, Eli Shechtman Towards Generalization and Simplicity in Continuous Control
Aravind Rajeswaran, Kendall Lowrey, Emanuel V. Todorov, Sham M. Kakade Training Quantized Nets: A Deeper Understanding
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein Translation Synchronization via Truncated Least Squares
Xiangru Huang, Zhenxiao Liang, Chandrajit Bajaj, Qixing Huang Triangle Generative Adversarial Networks
Zhe Gan, Liqun Chen, Weiyao Wang, Yuchen Pu, Yizhe Zhang, Hao Liu, Chunyuan Li, Lawrence Carin Trimmed Density Ratio Estimation
Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu Triple Generative Adversarial Nets
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Kristofer Bouchard, Alejandro Bujan, Fred Roosta, Shashanka Ubaru, Mr. Prabhat, Antoine Snijders, Jian-Hua Mao, Edward Chang, Michael W. Mahoney, Sharmodeep Bhattacharya Universal Style Transfer via Feature Transforms
Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang VAE Learning via Stein Variational Gradient Descent
Yuchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, Lawrence Carin Value Prediction Network
Junhyuk Oh, Satinder Singh, Honglak Lee Variable Importance Using Decision Trees
Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S Talwalkar Variational Inference via $\chi$ Upper Bound Minimization
Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Blei Variational Memory Addressing in Generative Models
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Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton Visual Interaction Networks: Learning a Physics Simulator from Video
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Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Le Song, Hongyuan Zha Welfare Guarantees from Data
Darrell Hoy, Denis Nekipelov, Vasilis Syrgkanis YASS: Yet Another Spike Sorter
Jin Hyung Lee, David E Carlson, Hooshmand Shokri Razaghi, Weichi Yao, Georges A Goetz, Espen Hagen, Eleanor Batty, E. J. Chichilnisky, Gaute T. Einevoll, Liam Paninski Z-Forcing: Training Stochastic Recurrent Networks
Anirudh Goyal ALIAS PARTH Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio Zap Q-Learning
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