Book
Advances in Neural Information Processing Systems 30 (NIPS 2017)
Edited by:
I. Guyon and U.V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett
Real Time Image Saliency for Black Box Classifiers Piotr Dabkowski, Yarin Gal
Joint distribution optimal transportation for domain adaptation Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy
Learning A Structured Optimal Bipartite Graph for Co-Clustering Feiping Nie, Xiaoqian Wang, Cheng Deng, Heng Huang
Learning to Inpaint for Image Compression Mohammad Haris Baig, Vladlen Koltun, Lorenzo Torresani
Inverse Filtering for Hidden Markov Models Robert Mattila, Cristian Rojas, Vikram Krishnamurthy, Bo Wahlberg
On clustering network-valued data Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization Omar El Housni, Vineet Goyal
Few-Shot Learning Through an Information Retrieval Lens Eleni Triantafillou, Richard Zemel, Raquel Urtasun
Accelerated consensus via Min-Sum Splitting Patrick Rebeschini, Sekhar C. Tatikonda
Saliency-based Sequential Image Attention with Multiset Prediction Sean Welleck, Jialin Mao, Kyunghyun Cho, Zheng Zhang
Adaptive Bayesian Sampling with Monte Carlo EM Anirban Roychowdhury, Srinivasan Parthasarathy
Scalable Levy Process Priors for Spectral Kernel Learning Phillip A. Jang, Andrew Loeb, Matthew Davidow, Andrew G. Wilson
Model-Powered Conditional Independence Test Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai
Learning Multiple Tasks with Multilinear Relationship Networks Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, Philip S. Yu
Query Complexity of Clustering with Side Information Arya Mazumdar, Barna Saha
Non-parametric Structured Output Networks Andreas Lehrmann, Leonid Sigal
Robust Imitation of Diverse Behaviors Ziyu Wang, Josh S. Merel, Scott E. Reed, Nando de Freitas, Gregory Wayne, Nicolas Heess
High-Order Attention Models for Visual Question Answering Idan Schwartz, Alexander Schwing, Tamir Hazan
FALKON: An Optimal Large Scale Kernel Method Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco
Generalized Linear Model Regression under Distance-to-set Penalties Jason Xu, Eric Chi, Kenneth Lange
Fisher GAN Youssef Mroueh, Tom Sercu
Minimax Estimation of Bandable Precision Matrices Addison Hu, Sahand Negahban
Kernel functions based on triplet comparisons Matthäus Kleindessner, Ulrike von Luxburg
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien
A New Theory for Matrix Completion Guangcan Liu, Qingshan Liu, Xiaotong Yuan
A Bayesian Data Augmentation Approach for Learning Deep Models Toan Tran, Trung Pham, Gustavo Carneiro, Lyle Palmer, Ian Reid
Deep Hyperalignment Muhammad Yousefnezhad, Daoqiang Zhang
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference Jonathan Huggins, Ryan P. Adams, Tamara Broderick
Online multiclass boosting Young Hun Jung, Jack Goetz, Ambuj Tewari
State Aware Imitation Learning Yannick Schroecker, Charles L. Isbell
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter Yi Xu, Qihang Lin, Tianbao Yang
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data Wei-Ning Hsu, Yu Zhang, James Glass
Recurrent Ladder Networks Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hao, Antti Rasmus, Rinu Boney, Harri Valpola
Distral: Robust multitask reinforcement learning Yee Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu
Real-Time Bidding with Side Information arthur flajolet, Patrick Jaillet
Learning Spherical Convolution for Fast Features from 360° Imagery Yu-Chuan Su, Kristen Grauman
Approximate Supermodularity Bounds for Experimental Design Luiz Chamon, Alejandro Ribeiro
Differentiable Learning of Logical Rules for Knowledge Base Reasoning Fan Yang, Zhilin Yang, William W. Cohen
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent Mert Gurbuzbalaban, Asuman Ozdaglar, Pablo A. Parrilo, Nuri Vanli
Principles of Riemannian Geometry in Neural Networks Michael Hauser, Asok Ray
Continual Learning with Deep Generative Replay Hanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon Kim
Nonlinear random matrix theory for deep learning Jeffrey Pennington, Pratik Worah
Identification of Gaussian Process State Space Models Stefanos Eleftheriadis, Tom Nicholson, Marc Deisenroth, James Hensman
Estimation of the covariance structure of heavy-tailed distributions Xiaohan Wei, Stanislav Minsker
Robust Optimization for Non-Convex Objectives Robert S. Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis
Exploring Generalization in Deep Learning Behnam Neyshabur, Srinadh Bhojanapalli, David Mcallester, Nati Srebro
Spherical convolutions and their application in molecular modelling Wouter Boomsma, Jes Frellsen
Safe Adaptive Importance Sampling Sebastian U. Stich, Anant Raj, Martin Jaggi
Introspective Classification with Convolutional Nets Long Jin, Justin Lazarow, Zhuowen Tu
Hybrid Reward Architecture for Reinforcement Learning Harm Van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, Jeffrey Tsang
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness Chris Russell, Matt J. Kusner, Joshua Loftus, Ricardo Silva
Dualing GANs Yujia Li, Alexander Schwing, Kuan-Chieh Wang, Richard Zemel
A Universal Analysis of Large-Scale Regularized Least Squares Solutions Ashkan Panahi, Babak Hassibi
Diffusion Approximations for Online Principal Component Estimation and Global Convergence Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms Cong Han Lim, Stephen Wright
Learning to Model the Tail Yu-Xiong Wang, Deva Ramanan, Martial Hebert
Neural Variational Inference and Learning in Undirected Graphical Models Volodymyr Kuleshov, Stefano Ermon
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification Bikash Joshi, Massih R. Amini, Ioannis Partalas, Franck Iutzeler, Yury Maximov
Learning Linear Dynamical Systems via Spectral Filtering Elad Hazan, Karan Singh, Cyril Zhang
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes Zhenwen Dai, Mauricio Álvarez, Neil Lawrence
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks Wei-Sheng Lai, Jia-Bin Huang, Ming-Hsuan Yang
Phase Transitions in the Pooled Data Problem Jonathan Scarlett, Volkan Cevher
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning Christoph Dann, Tor Lattimore, Emma Brunskill
Stein Variational Gradient Descent as Gradient Flow Qiang Liu
Expectation Propagation for t-Exponential Family Using q-Algebra Futoshi Futami, Issei Sato, Masashi Sugiyama
Collaborative PAC Learning Avrim Blum, Nika Haghtalab, Ariel D. Procaccia, Mingda Qiao
Polynomial time algorithms for dual volume sampling Chengtao Li, Stefanie Jegelka, Suvrit Sra
Premise Selection for Theorem Proving by Deep Graph Embedding Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng
Differentiable Learning of Submodular Models Josip Djolonga, Andreas Krause
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
Variational Laws of Visual Attention for Dynamic Scenes Dario Zanca, Marco Gori
How regularization affects the critical points in linear networks Amirhossein Taghvaei, Jin W. Kim, Prashant Mehta
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning Justin Fu, John Co-Reyes, Sergey Levine
Training Quantized Nets: A Deeper Understanding Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein
Convolutional Gaussian Processes Mark van der Wilk, Carl Edward Rasmussen, James Hensman
Best Response Regression Omer Ben-Porat, Moshe Tennenholtz
Elementary Symmetric Polynomials for Optimal Experimental Design Zelda E. Mariet, Suvrit Sra
Learning from Complementary Labels Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama
Dynamic Importance Sampling for Anytime Bounds of the Partition Function Qi Lou, Rina Dechter, Alexander T. Ihler
Process-constrained batch Bayesian optimisation Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin, Alessandra Sutti, Thomas Dorin, Murray Height, Paul Sanders, Svetha Venkatesh
Uprooting and Rerooting Higher-Order Graphical Models Mark Rowland, Adrian Weller
Learned in Translation: Contextualized Word Vectors Bryan McCann, James Bradbury, Caiming Xiong, Richard Socher
Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding Arya Mazumdar, Soumyabrata Pal
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, Bohyung Han
Few-Shot Adversarial Domain Adaptation Saeid Motiian, Quinn Jones, Seyed Iranmanesh, Gianfranco Doretto
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes Taylor W. Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez
Multi-View Decision Processes: The Helper-AI Problem Christos Dimitrakakis, David C. Parkes, Goran Radanovic, Paul Tylkin
Maximum Margin Interval Trees Alexandre Drouin, Toby Hocking, Francois Laviolette
Online Learning with a Hint Ofer Dekel, arthur flajolet, Nika Haghtalab, Patrick Jaillet
DPSCREEN: Dynamic Personalized Screening Kartik Ahuja, William Zame, Mihaela van der Schaar
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions M. Sevi Baltaoglu, Lang Tong, Qing Zhao
A-NICE-MC: Adversarial Training for MCMC Jiaming Song, Shengjia Zhao, Stefano Ermon
Question Asking as Program Generation Anselm Rothe, Brenden M. Lake, Todd Gureckis
Gradient Methods for Submodular Maximization Hamed Hassani, Mahdi Soltanolkotabi, Amin Karbasi
Recycling Privileged Learning and Distribution Matching for Fairness Novi Quadrianto, Viktoriia Sharmanska
Collecting Telemetry Data Privately Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin
Parallel Streaming Wasserstein Barycenters Matthew Staib, Sebastian Claici, Justin M. Solomon, Stefanie Jegelka
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition Mingrui Liu, Tianbao Yang
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall, Yarin Gal
Reconstruct & Crush Network Erinc Merdivan, Mohammad Reza Loghmani, Matthieu Geist
Permutation-based Causal Inference Algorithms with Interventions Yuhao Wang, Liam Solus, Karren Yang, Caroline Uhler
Deep Dynamic Poisson Factorization Model Chengyue Gong, win-bin huang
Scalable Generalized Linear Bandits: Online Computation and Hashing Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak, Rebecca Willett
Experimental Design for Learning Causal Graphs with Latent Variables Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
Lower bounds on the robustness to adversarial perturbations Jonathan Peck, Joris Roels, Bart Goossens, Yvan Saeys
Reliable Decision Support using Counterfactual Models Peter Schulam, Suchi Saria
Group Additive Structure Identification for Kernel Nonparametric Regression Chao Pan, Michael Zhu
A multi-agent reinforcement learning model of common-pool resource appropriation Julien Pérolat, Joel Z. Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel
Decoding with Value Networks for Neural Machine Translation Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tie-Yan Liu
Population Matching Discrepancy and Applications in Deep Learning Jianfei Chen, Chongxuan LI, Yizhong Ru, Jun Zhu
Predictive State Recurrent Neural Networks Carlton Downey, Ahmed Hefny, Byron Boots, Geoffrey J. Gordon, Boyue Li
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes Jeremiah Liu, Brent Coull
Sharpness, Restart and Acceleration Vincent Roulet, Alexandre d'Aspremont
Dynamic Routing Between Capsules Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations Yunzhu Li, Jiaming Song, Stefano Ermon
A Regularized Framework for Sparse and Structured Neural Attention Vlad Niculae, Mathieu Blondel
Style Transfer from Non-Parallel Text by Cross-Alignment Tianxiao Shen, Tao Lei, Regina Barzilay, Tommi Jaakkola
Unsupervised Learning of Disentangled Representations from Video Emily L. Denton, vighnesh Birodkar
Countering Feedback Delays in Multi-Agent Learning Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter W. Glynn, Claire Tomlin
Affinity Clustering: Hierarchical Clustering at Scale Mohammadhossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Raimondas Kiveris, Silvio Lattanzi, Vahab Mirrokni
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks Federico Monti, Michael Bronstein, Xavier Bresson
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz
f-GANs in an Information Geometric Nutshell Richard Nock, Zac Cranko, Aditya K. Menon, Lizhen Qu, Robert C. Williamson
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models Alex M. Lamb, Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio
Bayesian GAN Yunus Saatci, Andrew G. Wilson
Alternating minimization for dictionary learning with random initialization Niladri Chatterji, Peter L. Bartlett
Sparse Embedded $k$-Means Clustering Weiwei Liu, Xiaobo Shen, Ivor Tsang
Reducing Reparameterization Gradient Variance Andrew Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams
Min-Max Propagation Christopher Srinivasa, Inmar Givoni, Siamak Ravanbakhsh, Brendan J. Frey
Statistical Cost Sharing Eric Balkanski, Umar Syed, Sergei Vassilvitskii
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
The Expressive Power of Neural Networks: A View from the Width Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, Liwei Wang
Inverse Reward Design Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J. Russell, Anca Dragan
The power of absolute discounting: all-dimensional distribution estimation Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky, Venkatadheeraj Pichapati
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
Spectral Mixture Kernels for Multi-Output Gaussian Processes Gabriel Parra, Felipe Tobar
Affine-Invariant Online Optimization and the Low-rank Experts Problem Tomer Koren, Roi Livni
Pose Guided Person Image Generation Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool
Successor Features for Transfer in Reinforcement Learning Andre Barreto, Will Dabney, Remi Munos, Jonathan J. Hunt, Tom Schaul, Hado P. van Hasselt, David Silver
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning Xingguo Li, Lin Yang, Jason Ge, Jarvis Haupt, Tong Zhang, Tuo Zhao
Hypothesis Transfer Learning via Transformation Functions Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu
Variational Inference via $\chi$ Upper Bound Minimization Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Blei
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks Qinliang Su, xuejun Liao, Lawrence Carin
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation Yuhuai Wu, Elman Mansimov, Roger B. Grosse, Shun Liao, Jimmy Ba
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds Shipra Agrawal, Randy Jia
Efficient Second-Order Online Kernel Learning with Adaptive Embedding Daniele Calandriello, Alessandro Lazaric, Michal Valko
Solving Most Systems of Random Quadratic Equations Gang Wang, Georgios Giannakis, Yousef Saad, Jie Chen
Online Reinforcement Learning in Stochastic Games Chen-Yu Wei, Yi-Te Hong, Chi-Jen Lu
Independence clustering (without a matrix) Daniil Ryabko
Effective Parallelisation for Machine Learning Michael Kamp, Mario Boley, Olana Missura, Thomas Gärtner
Deep Mean-Shift Priors for Image Restoration Siavash Arjomand Bigdeli, Matthias Zwicker, Paolo Favaro, Meiguang Jin
On Structured Prediction Theory with Calibrated Convex Surrogate Losses Anton Osokin, Francis Bach, Simon Lacoste-Julien
Invariance and Stability of Deep Convolutional Representations Alberto Bietti, Julien Mairal
Variational Memory Addressing in Generative Models Jörg Bornschein, Andriy Mnih, Daniel Zoran, Danilo Jimenez Rezende
Shallow Updates for Deep Reinforcement Learning Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Learning with Bandit Feedback in Potential Games Amélie Heliou, Johanne Cohen, Panayotis Mertikopoulos
A Greedy Approach for Budgeted Maximum Inner Product Search Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei, Inderjit S. Dhillon
Riemannian approach to batch normalization Minhyung Cho, Jaehyung Lee
Adaptive Clustering through Semidefinite Programming Martin Royer
#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
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi
Online Prediction with Selfish Experts Tim Roughgarden, Okke Schrijvers
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach Slobodan Mitrovic, Ilija Bogunovic, Ashkan Norouzi-Fard, Jakub M. Tarnawski, Volkan Cevher
Neural Program Meta-Induction Jacob Devlin, Rudy R. Bunel, Rishabh Singh, Matthew Hausknecht, Pushmeet Kohli
The Scaling Limit of High-Dimensional Online Independent Component Analysis Chuang Wang, Yue Lu
Practical Locally Private Heavy Hitters Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta
Mixture-Rank Matrix Approximation for Collaborative Filtering Dongsheng Li, Chao Chen, Wei Liu, Tun Lu, Ning Gu, Stephen Chu
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James L. Sharpnack, Ryan J. Tibshirani
Robust Conditional Probabilities Yoav Wald, Amir Globerson
Attention is All you Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin
A General Framework for Robust Interactive Learning Ehsan Emamjomeh-Zadeh, David Kempe
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions Maria-Florina F. Balcan, Hongyang Zhang
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee Alireza Aghasi, Afshin Abdi, Nam Nguyen, Justin Romberg
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, C. Lawrence Zitnick
Task-based End-to-end Model Learning in Stochastic Optimization Priya Donti, Brandon Amos, J. Zico Kolter
Fader Networks:Manipulating Images by Sliding Attributes Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic DENOYER, Marc'Aurelio Ranzato
VAE Learning via Stein Variational Gradient Descent Yuchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, Lawrence Carin
Approximation and Convergence Properties of Generative Adversarial Learning Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton
Local Aggregative Games Vikas Garg, Tommi Jaakkola
An Error Detection and Correction Framework for Connectomics Jonathan Zung, Ignacio Tartavull, Kisuk Lee, H. Sebastian Seung
Hindsight Experience Replay Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, Wojciech Zaremba
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data Joel A. Tropp, Alp Yurtsever, Madeleine Udell, Volkan Cevher
The Numerics of GANs Lars Mescheder, Sebastian Nowozin, Andreas Geiger
Cortical microcircuits as gated-recurrent neural networks Rui Costa, Ioannis Alexandros Assael, Brendan Shillingford, Nando de Freitas, TIm Vogels
Deep Lattice Networks and Partial Monotonic Functions Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta
Zap Q-Learning Adithya M Devraj, Sean Meyn
Contrastive Learning for Image Captioning Bo Dai, Dahua Lin
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net Anirudh Goyal ALIAS PARTH GOYAL, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio
Linear Time Computation of Moments in Sum-Product Networks Han Zhao, Geoffrey J. Gordon
SGD Learns the Conjugate Kernel Class of the Network Amit Daniely
Learning to Pivot with Adversarial Networks Gilles Louppe, Michael Kagan, Kyle Cranmer
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration Jason Altschuler, Jonathan Niles-Weed, Philippe Rigollet
Universal Style Transfer via Feature Transforms Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang
Ensemble Sampling Xiuyuan Lu, Benjamin Van Roy
Practical Data-Dependent Metric Compression with Provable Guarantees Piotr Indyk, Ilya Razenshteyn, Tal Wagner
Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery Jie Shen, Ping Li
Selective Classification for Deep Neural Networks Yonatan Geifman, Ran El-Yaniv
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space Liwei Wang, Alexander Schwing, Svetlana Lazebnik
Deconvolutional Paragraph Representation Learning Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, Lawrence Carin
Learning to See Physics via Visual De-animation Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum
Adversarial Symmetric Variational Autoencoder Yuchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin
Model evidence from nonequilibrium simulations Michael Habeck
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang, Han Liu
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data Stéphanie ALLASSONNIERE, Juliette Chevallier, Stephane Oudard
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks Kyuhong Shim, Minjae Lee, Iksoo Choi, Yoonho Boo, Wonyong Sung
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter
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
Action Centered Contextual Bandits Kristjan Greenewald, Ambuj Tewari, Susan Murphy, Predag Klasnja
Cost efficient gradient boosting Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks Surbhi Goel, Adam Klivans
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models Adarsh Prasad, Alexandru Niculescu-Mizil, Pradeep K. Ravikumar
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Mr. Prabhat, Chris Pal
A Meta-Learning Perspective on Cold-Start Recommendations for Items Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar, Rahul Jain
Deep Hyperspherical Learning Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts Raymond Yeh, Jinjun Xiong, Wen-Mei Hwu, Minh Do, Alexander Schwing
Off-policy evaluation for slate recommendation Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, Imed Zitouni
Unbiased estimates for linear regression via volume sampling Michal Derezinski, Manfred K. K. Warmuth
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces Songbai Yan, Chicheng Zhang
Renyi Differential Privacy Mechanisms for Posterior Sampling Joseph Geumlek, Shuang Song, Kamalika Chaudhuri
Variable Importance Using Decision Trees Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar
A simple model of recognition and recall memory Nisheeth Srivastava, Edward Vul
Implicit Regularization in Matrix Factorization Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro
Continuous DR-submodular Maximization: Structure and Algorithms An Bian, Kfir Levy, Andreas Krause, Joachim M. Buhmann
Decoupling "when to update" from "how to update" Eran Malach, Shai Shalev-Shwartz
Regret Analysis for Continuous Dueling Bandit Wataru Kumagai
One-Sided Unsupervised Domain Mapping Sagie Benaim, Lior Wolf
Poincaré Embeddings for Learning Hierarchical Representations Maximillian Nickel, Douwe Kiela
Variance-based Regularization with Convex Objectives Hongseok Namkoong, John C. Duchi
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening Kevin Lin, James L. Sharpnack, Alessandro Rinaldo, Ryan J. Tibshirani
Cross-Spectral Factor Analysis Neil Gallagher, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, David E. Carlson
Self-Normalizing Neural Networks Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders Artur Speiser, Jinyao Yan, Evan W. Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke
Asynchronous Parallel Coordinate Minimization for MAP Inference Ofer Meshi, Alexander Schwing
Inductive Representation Learning on Large Graphs Will Hamilton, Zhitao Ying, Jure Leskovec
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs Rowan McAllister, Carl Edward Rasmussen
Coded Distributed Computing for Inverse Problems Yaoqing Yang, Pulkit Grover, Soummya Kar
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions Ryan J. Tibshirani
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems Ingmar Kanitscheider, Ila Fiete
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud Zahra Ghodsi, Tianyu Gu, Siddharth Garg
Improved Graph Laplacian via Geometric Self-Consistency Dominique Joncas, Marina Meila, James McQueen
Generalization Properties of Learning with Random Features Alessandro Rudi, Lorenzo Rosasco
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
Federated Multi-Task Learning Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet S. Talwalkar
Learning Causal Structures Using Regression Invariance AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction Søren Dahlgaard, Mathias Knudsen, Mikkel Thorup
Gaussian Quadrature for Kernel Features Tri Dao, Christopher M. De Sa, Christopher Ré
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, Joseph J. Lim
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees Francesco Locatello, Michael Tschannen, Gunnar Raetsch, Martin Jaggi
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks Arturs Backurs, Piotr Indyk, Ludwig Schmidt
Acceleration and Averaging in Stochastic Descent Dynamics Walid Krichene, Peter L. Bartlett
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
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process Hongyuan Mei, Jason M. Eisner
Bayesian Optimization with Gradients Jian Wu, Matthias Poloczek, Andrew G. Wilson, Peter Frazier
Visual Reference Resolution using Attention Memory for Visual Dialog Paul Hongsuck Seo, Andreas Lehrmann, Bohyung Han, Leonid Sigal
Straggler Mitigation in Distributed Optimization Through Data Encoding Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation Zhaohan Guo, Philip S. Thomas, Emma Brunskill
Attentional Pooling for Action Recognition Rohit Girdhar, Deva Ramanan
Testing and Learning on Distributions with Symmetric Noise Invariance Ho Chung Law, Christopher Yau, Dino Sejdinovic
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen, Harri Valpola
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments Ryan Lowe, YI WU, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, Igor Mordatch
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning Liangpeng Zhang, Ke Tang, Xin Yao
Bayesian Compression for Deep Learning Christos Louizos, Karen Ullrich, Max Welling
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? Cameron Musco, David Woodruff
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks Ziming Zhang, Matthew Brand
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes Ahmed M. Alaa, Mihaela van der Schaar
Learning Overcomplete HMMs Vatsal Sharan, Sham M. Kakade, Percy S. Liang, Gregory Valiant
Convolutional Phase Retrieval Qing Qu, Yuqian Zhang, Yonina Eldar, John Wright
Stochastic and Adversarial Online Learning without Hyperparameters Ashok Cutkosky, Kwabena A. Boahen
Masked Autoregressive Flow for Density Estimation George Papamakarios, Theo Pavlakou, Iain Murray
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic
Learning Hierarchical Information Flow with Recurrent Neural Modules Danijar Hafner, Alexander Irpan, James Davidson, Nicolas Heess
Deanonymization in the Bitcoin P2P Network Giulia Fanti, Pramod Viswanath
Learning with Average Top-k Loss Yanbo Fan, Siwei Lyu, Yiming Ying, Baogang Hu
MaskRNN: Instance Level Video Object Segmentation Yuan-Ting Hu, Jia-Bin Huang, Alexander Schwing
Max-Margin Invariant Features from Transformed Unlabelled Data Dipan Pal, Ashwin Kannan, Gautam Arakalgud, Marios Savvides
Sparse Approximate Conic Hulls Greg Van Buskirk, Benjamin Raichel, Nicholas Ruozzi
Label Distribution Learning Forests Wei Shen, KAI ZHAO, Yilu Guo, Alan L. Yuille
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
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, Licheng Jiao
Hierarchical Implicit Models and Likelihood-Free Variational Inference Dustin Tran, Rajesh Ranganath, David Blei
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding Mainak Jas, Tom Dupré la Tour, Umut Simsekli, Alexandre Gramfort
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Discriminative State Space Models Vitaly Kuznetsov, Mehryar Mohri
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols Serhii Havrylov, Ivan Titov
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization Ahmet Alacaoglu, Quoc Tran Dinh, Olivier Fercoq, Volkan Cevher
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Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stephane Mallat
On Frank-Wolfe and Equilibrium Computation Jacob D. Abernethy, Jun-Kun Wang
Generalizing GANs: A Turing Perspective Roderich Gross, Yue Gu, Wei Li, Melvin Gauci
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
A Screening Rule for l1-Regularized Ising Model Estimation Zhaobin Kuang, Sinong Geng, David Page
A Minimax Optimal Algorithm for Crowdsourcing Thomas Bonald, Richard Combes
Communication-Efficient Distributed Learning of Discrete Distributions Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt
VAIN: Attentional Multi-agent Predictive Modeling Yedid Hoshen
Hierarchical Attentive Recurrent Tracking Adam Kosiorek, Alex Bewley, Ingmar Posner
Sobolev Training for Neural Networks Wojciech M. Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Swirszcz, Razvan Pascanu
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization Tomoya Murata, Taiji Suzuki
Learning with Feature Evolvable Streams Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou
Safe Model-based Reinforcement Learning with Stability Guarantees Felix Berkenkamp, Matteo Turchetta, Angela Schoellig, Andreas Krause
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis Kristjan Greenewald, Seyoung Park, Shuheng Zhou, Alexander Giessing
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet
Context Selection for Embedding Models Liping Liu, Francisco Ruiz, Susan Athey, David Blei
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction Kristofer Bouchard, Alejandro Bujan, Fred Roosta, Shashanka Ubaru, Mr. Prabhat, Antoine Snijders, Jian-Hua Mao, Edward Chang, Michael W. Mahoney, Sharmodeep Bhattacharya
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Targeting EEG/LFP Synchrony with Neural Nets Yitong Li, michael Murias, samantha Major, geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E. Carlson
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes Anton Mallasto, Aasa Feragen
Online Dynamic Programming Holakou Rahmanian, Manfred K. K. Warmuth
Neural Discrete Representation Learning Aaron van den Oord, Oriol Vinyals, koray kavukcuoglu
Probabilistic Rule Realization and Selection Haizi Yu, Tianxi Li, Lav R. Varshney
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther
Stabilizing Training of Generative Adversarial Networks through Regularization Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann
Training Deep Networks without Learning Rates Through Coin Betting Francesco Orabona, Tatiana Tommasi
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
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation Christian Borgs, Jennifer Chayes, Christina E. Lee, Devavrat Shah
Positive-Unlabeled Learning with Non-Negative Risk Estimator Ryuichi Kiryo, Gang Niu, Marthinus C. du Plessis, Masashi Sugiyama
Gradient descent GAN optimization is locally stable Vaishnavh Nagarajan, J. Zico Kolter
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers Cong Fang, Feng Cheng, Zhouchen Lin
Group Sparse Additive Machine Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang
PixelGAN Autoencoders Alireza Makhzani, Brendan J. Frey
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models Rishit Sheth, Roni Khardon
Online control of the false discovery rate with decaying memory Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan
Safe and Nested Subgame Solving for Imperfect-Information Games Noam Brown, Tuomas Sandholm
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent Ben London
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer
Toward Multimodal Image-to-Image Translation Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, Benjamin Recht
Mean Field Residual Networks: On the Edge of Chaos Ge Yang, Samuel Schoenholz
Non-convex Finite-Sum Optimization Via SCSG Methods Lihua Lei, Cheng Ju, Jianbo Chen, Michael I. Jordan
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization Aryan Mokhtari, Alejandro Ribeiro
Doubly Stochastic Variational Inference for Deep Gaussian Processes Hugh Salimbeni, Marc Deisenroth
From Parity to Preference-based Notions of Fairness in Classification Muhammad Bilal Zafar, Isabel Valera, Manuel Rodriguez, Krishna Gummadi, Adrian Weller
Nonparametric Online Regression while Learning the Metric Ilja Kuzborskij, Nicolò Cesa-Bianchi
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure Alberto Bietti, Julien Mairal
Working hard to know your neighbor's margins: Local descriptor learning loss Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, Jiri Matas
Hiding Images in Plain Sight: Deep Steganography Shumeet Baluja
Lookahead Bayesian Optimization with Inequality Constraints Remi Lam, Karen Willcox
Online Learning with Transductive Regret Mehryar Mohri, Scott Yang
Pixels to Graphs by Associative Embedding Alejandro Newell, Jia Deng
Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex Chaobing Song, Shaobo Cui, Yong Jiang, Shu-Tao Xia
Reinforcement Learning under Model Mismatch Aurko Roy, Huan Xu, Sebastian Pokutta
Concrete Dropout Yarin Gal, Jiri Hron, Alex Kendall
Multiresolution Kernel Approximation for Gaussian Process Regression Yi Ding, Risi Kondor, Jonathan Eskreis-Winkler
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem Yasin Abbasi Yadkori, Peter L. Bartlett, Victor Gabillon
Learned D-AMP: Principled Neural Network based Compressive Image Recovery Chris Metzler, Ali Mousavi, Richard Baraniuk
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks
Unsupervised Transformation Learning via Convex Relaxations Tatsunori B. Hashimoto, Percy S. Liang, John C. Duchi
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
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Steven Z. Wu
Triple Generative Adversarial Nets Chongxuan LI, Taufik Xu, Jun Zhu, Bo Zhang
Deep Learning with Topological Signatures Christoph Hofer, Roland Kwitt, Marc Niethammer, Andreas Uhl
Revenue Optimization with Approximate Bid Predictions Andres Munoz, Sergei Vassilvitskii
Mapping distinct timescales of functional interactions among brain networks Mali Sundaresan, Arshed Nabeel, Devarajan Sridharan
Improved Training of Wasserstein GANs Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron C. Courville
Adaptive stimulus selection for optimizing neural population responses Benjamin Cowley, Ryan Williamson, Katerina Clemens, Matthew Smith, Byron M. Yu
Matrix Norm Estimation from a Few Entries Ashish Khetan, Sewoong Oh
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems Simon S. Du, Yining Wang, Aarti Singh
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li
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
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg, Su-In Lee
Nonbacktracking Bounds on the Influence in Independent Cascade Models Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls Zeyuan Allen-Zhu, Elad Hazan, Wei Hu, Yuanzhi Li
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach Roel Dobbe, David Fridovich-Keil, Claire Tomlin
Neural system identification for large populations separating “what” and “where” David Klindt, Alexander S. Ecker, Thomas Euler, Matthias Bethge
Learning Active Learning from Data Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
Controllable Invariance through Adversarial Feature Learning Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig
Visual Interaction Networks: Learning a Physics Simulator from Video Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, Andrea Tacchetti
Repeated Inverse Reinforcement Learning Kareem Amin, Nan Jiang, Satinder Singh
Inference in Graphical Models via Semidefinite Programming Hierarchies Murat A. Erdogdu, Yash Deshpande, Andrea Montanari
Gauging Variational Inference Sung-Soo Ahn, Michael Chertkov, Jinwoo Shin
Teaching Machines to Describe Images with Natural Language Feedback huan ling, Sanja Fidler
Associative Embedding: End-to-End Learning for Joint Detection and Grouping Alejandro Newell, Zhiao Huang, Jia Deng
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications Linus Hamilton, Frederic Koehler, Ankur Moitra
Subset Selection and Summarization in Sequential Data Ehsan Elhamifar, M. Clara De Paolis Kaluza
Z-Forcing: Training Stochastic Recurrent Networks Anirudh Goyal ALIAS PARTH GOYAL, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio
Regret Minimization in MDPs with Options without Prior Knowledge Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity Asish Ghoshal, Jean Honorio
Learning Neural Representations of Human Cognition across Many fMRI Studies Arthur Mensch, Julien Mairal, Danilo Bzdok, Bertrand Thirion, Gael Varoquaux
Conic Scan-and-Cover algorithms for nonparametric topic modeling Mikhail Yurochkin, Aritra Guha, XuanLong Nguyen
Online Learning for Multivariate Hawkes Processes Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
An Empirical Study on The Properties of Random Bases for Kernel Methods Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Fei Sha
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions Aryeh Kontorovich, Sivan Sabato, Roi Weiss
Causal Effect Inference with Deep Latent-Variable Models Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach Emmanouil Platanios, Hoifung Poon, Tom M. Mitchell, Eric J. Horvitz
A Decomposition of Forecast Error in Prediction Markets Miro Dudik, Sebastien Lahaie, Ryan M. Rogers, Jennifer Wortman Vaughan
Ranking Data with Continuous Labels through Oriented Recursive Partitions Stéphan Clémençon, Mastane Achab
Scalable Log Determinants for Gaussian Process Kernel Learning Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew G. Wilson
Fair Clustering Through Fairlets Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii
A Linear-Time Kernel Goodness-of-Fit Test Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton
Rotting Bandits Nir Levine, Koby Crammer, Shie Mannor
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains Ga Wu, Buser Say, Scott Sanner
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models Chris Oates, Steven Niederer, Angela Lee, François-Xavier Briol, Mark Girolami
Bandits Dueling on Partially Ordered Sets Julien Audiffren, Liva Ralaivola
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search Mohammad Ali Bashiri, Xinhua Zhang
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces Daniel Milstein, Jason Pacheco, Leigh Hochberg, John D. Simeral, Beata Jarosiewicz, Erik Sudderth
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization Jeffrey Regier, Michael I. Jordan, Jon McAuliffe
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network Lixin Fan
Optimized Pre-Processing for Discrimination Prevention Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
Scalable Demand-Aware Recommendation Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li
Learning a Multi-View Stereo Machine Abhishek Kar, Christian Häne, Jitendra Malik
On Blackbox Backpropagation and Jacobian Sensing Krzysztof M. Choromanski, Vikas Sindhwani
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
GP CaKe: Effective brain connectivity with causal kernels Luca Ambrogioni, Max Hinne, Marcel Van Gerven, Eric Maris
Certified Defenses for Data Poisoning Attacks Jacob Steinhardt, Pang Wei W. Koh, Percy S. Liang
Towards Generalization and Simplicity in Continuous Control Aravind Rajeswaran, Kendall Lowrey, Emanuel V. Todorov, Sham M. Kakade
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
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
Adaptive Active Hypothesis Testing under Limited Information Fabio Cecchi, Nidhi Hegde
Translation Synchronization via Truncated Least Squares Xiangru Huang, Zhenxiao Liang, Chandrajit Bajaj, Qixing Huang
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization Yossi Arjevani
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
Recursive Sampling for the Nystrom Method Cameron Musco, Christopher Musco
Early stopping for kernel boosting algorithms: A general analysis with localized complexities Yuting Wei, Fanny Yang, Martin J. Wainwright
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning Shixiang (Shane) Gu, Timothy Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine
Parameter-Free Online Learning via Model Selection Dylan J. Foster, Satyen Kale, Mehryar Mohri, Karthik Sridharan
Predicting User Activity Level In Point Processes With Mass Transport Equation Yichen Wang, Xiaojing Ye, Hongyuan Zha, Le Song
The Importance of Communities for Learning to Influence Eric Balkanski, Nicole Immorlica, Yaron Singer
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra John T. Halloran, David M. Rocke
On the Optimization Landscape of Tensor Decompositions Rong Ge, Tengyu Ma
Counterfactual Fairness Matt J. Kusner, Joshua Loftus, Chris Russell, Ricardo Silva
Efficient Online Linear Optimization with Approximation Algorithms Dan Garber
Inhomogeneous Hypergraph Clustering with Applications Pan Li, Olgica Milenkovic
Runtime Neural Pruning Ji Lin, Yongming Rao, Jiwen Lu, Jie Zhou
Train longer, generalize better: closing the generalization gap in large batch training of neural networks Elad Hoffer, Itay Hubara, Daniel Soudry
Monte-Carlo Tree Search by Best Arm Identification Emilie Kaufmann, Wouter M. Koolen
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
Scalable Model Selection for Belief Networks Zhao Song, Yusuke Muraoka, Ryohei Fujimaki, Lawrence Carin
Collaborative Deep Learning in Fixed Topology Networks Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar
On the Complexity of Learning Neural Networks Le Song, Santosh Vempala, John Wilmes, Bo Xie
A Sample Complexity Measure with Applications to Learning Optimal Auctions Vasilis Syrgkanis
On Optimal Generalizability in Parametric Learning Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, Vahid Tarokh
K-Medoids For K-Means Seeding James Newling, François Fleuret
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction Dan Xu, Wanli Ouyang, Xavier Alameda-Pineda, Elisa Ricci, Xiaogang Wang, Nicu Sebe
Geometric Descent Method for Convex Composite Minimization Shixiang Chen, Shiqian Ma, Wei Liu
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks Zelun Luo, Yuliang Zou, Judy Hoffman, Li F. Fei-Fei
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications Qinshi Wang, Wei Chen
Matching neural paths: transfer from recognition to correspondence search Nikolay Savinov, Lubor Ladicky, Marc Pollefeys
Convergence Analysis of Two-layer Neural Networks with ReLU Activation Yuanzhi Li, Yang Yuan
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
Self-supervised Learning of Motion Capture Hsiao-Yu Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System Chengxu Zhuang, Jonas Kubilius, Mitra JZ Hartmann, Daniel L. Yamins
Clustering Billions of Reads for DNA Data Storage Cyrus Rashtchian, Konstantin Makarychev, Miklos Racz, Siena Ang, Djordje Jevdjic, Sergey Yekhanin, Luis Ceze, Karin Strauss
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms Marco Cusumano-Towner, Vikash K. Mansinghka
Information-theoretic analysis of generalization capability of learning algorithms Aolin Xu, Maxim Raginsky
MarrNet: 3D Shape Reconstruction via 2.5D Sketches Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, Josh Tenenbaum
Flexible statistical inference for mechanistic models of neural dynamics Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching Chunyuan Li, Hao Liu, Changyou Chen, Yuchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization Pan Xu, Jian Ma, Quanquan Gu
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
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
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models Caglar Gulcehre, Francis Dutil, Adam Trischler, Yoshua Bengio
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems Yonatan Belinkov, James Glass
Multi-Task Learning for Contextual Bandits Aniket Anand Deshmukh, Urun Dogan, Clay Scott
Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks Prateep Bhattacharjee, Sukhendu Das
Improving the Expected Improvement Algorithm Chao Qin, Diego Klabjan, Daniel Russo
Towards Accurate Binary Convolutional Neural Network Xiaofan Lin, Cong Zhao, Wei Pan
Spectrally-normalized margin bounds for neural networks Peter L. Bartlett, Dylan J. Foster, Matus J. Telgarsky
Consistent Multitask Learning with Nonlinear Output Relations Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
Boltzmann Exploration Done Right Nicolò Cesa-Bianchi, Claudio Gentile, Gabor Lugosi, Gergely Neu
End-to-end Differentiable Proving Tim Rocktäschel, Sebastian Riedel
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation Sheng Li, Yun Fu
Tomography of the London Underground: a Scalable Model for Origin-Destination Data Nicolò Colombo, Ricardo Silva, Soong Moon Kang
Gaussian process based nonlinear latent structure discovery in multivariate spike train data Anqi Wu, Nicholas A. Roy, Stephen Keeley, Jonathan W. Pillow
Multi-Objective Non-parametric Sequential Prediction Guy Uziel, Ran El-Yaniv
Optimal Sample Complexity of M-wise Data for Top-K Ranking Minje Jang, Sunghyun Kim, Changho Suh, Sewoong Oh
From which world is your graph Cheng Li, Felix MF Wong, Zhenming Liu, Varun Kanade
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms James McInerney
Multiscale Quantization for Fast Similarity Search Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N. Holtmann-Rice, David Simcha, Felix Yu
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Perturbative Black Box Variational Inference Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
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