Advances in Neural Information Processing Systems 30

NIPS 2017

Edited by I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett

ISBN: 9781510860964

679 papers

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, Alex 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, Nicholas 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 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. 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 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
Modulating early visual processing by language Harm de Vries, Florian Strub, Jeremie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C. Courville
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
Linearly constrained Gaussian processes Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B Schön
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
Good Semi-supervised Learning That Requires a Bad GAN Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, Ruslan Salakhutdinov
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. 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 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 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
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction Zhan Shi, Xinhua Zhang, Yaoliang Yu
Perturbative Black Box Variational Inference Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
Kernel Feature Selection via Conditional Covariance Minimization Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I Jordan
Active Learning from Peers Keerthiram Murugesan, Jaime Carbonell
On Fairness and Calibration Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, Kilian Q. Weinberger
One-Shot Imitation Learning Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
Triangle Generative Adversarial Networks Zhe Gan, Liqun Chen, Weiyao Wang, Yuchen Pu, Yizhe Zhang, Hao Liu, Chunyuan Li, Lawrence Carin
Learning Populations of Parameters Kevin Tian, Weihao Kong, Gregory Valiant
Multi-Armed Bandits with Metric Movement Costs Tomer Koren, Roi Livni, Yishay Mansour
Structured Embedding Models for Grouped Data Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei
Conservative Contextual Linear Bandits Abbas Kazerouni, Mohammad Ghavamzadeh, Yasin Abbasi Yadkori, Benjamin Van Roy
Regularized Modal Regression with Applications in Cognitive Impairment Prediction Xiaoqian Wang, Hong Chen, Weidong Cai, Dinggang Shen, Heng Huang
Adversarial Ranking for Language Generation Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, Ming-ting Sun
Diving into the shallows: a computational perspective on large-scale shallow learning SIYUAN MA, Mikhail Belkin
Integration Methods and Optimization Algorithms Damien Scieur, Vincent Roulet, Francis Bach, Alexandre d'Aspremont
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings Krzysztof M Choromanski, Mark Rowland, Adrian Weller
A KL-LUCB algorithm for Large-Scale Crowdsourcing Ervin Tanczos, Robert Nowak, Bob Mankoff
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly Ethan Elenberg, Alexandros G Dimakis, Moran Feldman, Amin Karbasi
Decomposable Submodular Function Minimization: Discrete and Continuous Alina Ene, Huy Nguyen, László A. Végh
Learning Affinity via Spatial Propagation Networks Sifei Liu, Shalini De Mello, Jinwei Gu, Guangyu Zhong, Ming-Hsuan Yang, Jan Kautz
Gated Recurrent Convolution Neural Network for OCR Jianfeng Wang, Xiaolin Hu
Multi-view Matrix Factorization for Linear Dynamical System Estimation Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvari
Policy Gradient With Value Function Approximation For Collective Multiagent Planning Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Stochastic Submodular Maximization: The Case of Coverage Functions Mohammad Karimi, Mario Lucic, Hamed Hassani, Andreas Krause
Stochastic Approximation for Canonical Correlation Analysis Raman Arora, Teodor Vanislavov Marinov, Poorya Mianjy, Nati Srebro
Linear regression without correspondence Daniel J. Hsu, Kevin Shi, Xiaorui Sun
Structured Generative Adversarial Networks Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P Xing
Dynamic-Depth Context Tree Weighting Joao V Messias, Shimon Whiteson
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval Gauri Jagatap, Chinmay Hegde
Hierarchical Methods of Moments Matteo Ruffini, Guillaume Rabusseau, Borja Balle
A New Alternating Direction Method for Linear Programming Sinong Wang, Ness Shroff
Near Optimal Sketching of Low-Rank Tensor Regression Xingguo Li, Jarvis Haupt, David Woodruff
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models Sergey Ioffe
Position-based Multiple-play Bandit Problem with Unknown Position Bias Junpei Komiyama, Junya Honda, Akiko Takeda
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
Eigen-Distortions of Hierarchical Representations Alexander Berardino, Valero Laparra, Johannes Ballé, Eero Simoncelli
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon Xin Dong, Shangyu Chen, Sinno Pan
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding Yingce Xia, Fei Tian, Lijun Wu, Jianxin Lin, Tao Qin, Nenghai Yu, Tie-Yan Liu
Do Deep Neural Networks Suffer from Crowding? Anna Volokitin, Gemma Roig, Tomaso A Poggio
Non-Stationary Spectral Kernels Sami Remes, Markus Heinonen, Samuel Kaski
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H Macke
Minimizing a Submodular Function from Samples Eric Balkanski, Yaron Singer
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
Adversarial Surrogate Losses for Ordinal Regression Rizal Fathony, Mohammad Ali Bashiri, Brian Ziebart
Self-Supervised Intrinsic Image Decomposition Michael Janner, Jiajun Wu, Tejas D Kulkarni, Ilker Yildirim, Josh Tenenbaum
On-the-fly Operation Batching in Dynamic Computation Graphs Graham Neubig, Yoav Goldberg, Chris Dyer
Fitting Low-Rank Tensors in Constant Time Kohei Hayashi, Yuichi Yoshida
Random Projection Filter Bank for Time Series Data Amir-massoud Farahmand, Sepideh Pourazarm, Daniel Nikovski
Dynamic Revenue Sharing Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, IIIS Song Zuo
Prototypical Networks for Few-shot Learning Jake Snell, Kevin Swersky, Richard Zemel
Unsupervised learning of object frames by dense equivariant image labelling James Thewlis, Hakan Bilen, Andrea Vedaldi
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays Cesar F. Caiafa, Olaf Sporns, Andrew Saykin, Franco Pestilli
Random Permutation Online Isotonic Regression Wojciech Kotlowski, Wouter M. Koolen, Alan Malek
PRUNE: Preserving Proximity and Global Ranking for Network Embedding Yi-An Lai, Chin-Chi Hsu, Wen Hao Chen, Mi-Yen Yeh, Shou-De Lin
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes Kfir Levy
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network Wengong Jin, Connor Coley, Regina Barzilay, Tommi Jaakkola
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
Improved Dynamic Regret for Non-degenerate Functions Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-Hua Zhou
AdaGAN: Boosting Generative Models Ilya O Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann SIMON-GABRIEL, Bernhard Schölkopf
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences Kinjal Basu, Ankan Saha, Shaunak Chatterjee
Graph Matching via Multiplicative Update Algorithm Bo Jiang, Jin Tang, Chris Ding, Yihong Gong, Bin Luo
Neural Expectation Maximization Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs Saurabh Verma, Zhi-Li Zhang
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems Le Fang, Fan Yang, Wen Dong, Tong Guan, Chunming Qiao
Welfare Guarantees from Data Darrell Hoy, Denis Nekipelov, Vasilis Syrgkanis
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference Abhishek Kumar, Prasanna Sattigeri, Tom Fletcher
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples Moustapha M Cisse, Yossi Adi, Natalia Neverova, Joseph Keshet
Clustering Stable Instances of Euclidean k-means. Aravindan Vijayaraghavan, Abhratanu Dutta, Alex Wang
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
Deep Reinforcement Learning from Human Preferences Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, Dario Amodei
Subset Selection under Noise Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space Charles Ruizhongtai Qi, Li Yi, Hao Su, Leonidas Guibas
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search Benjamin Moseley, Joshua Wang
Thinking Fast and Slow with Deep Learning and Tree Search Thomas Anthony, Zheng Tian, David Barber
Learning Combinatorial Optimization Algorithms over Graphs Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, Le Song
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice Jeffrey Pennington, Samuel Schoenholz, Surya Ganguli
Adaptive Classification for Prediction Under a Budget Feng Nan, Venkatesh Saligrama
Online Convex Optimization with Stochastic Constraints Hao Yu, Michael Neely, Xiaohan Wei
Structured Bayesian Pruning via Log-Normal Multiplicative Noise Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P Vetrov
Clustering with Noisy Queries Arya Mazumdar, Barna Saha
Compression-aware Training of Deep Networks Jose M Alvarez, Mathieu Salzmann
Maxing and Ranking with Few Assumptions Moein Falahatgar, Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar
Subspace Clustering via Tangent Cones Amin Jalali, Rebecca Willett
DropoutNet: Addressing Cold Start in Recommender Systems Maksims Volkovs, Guangwei Yu, Tomi Poutanen
Unsupervised Image-to-Image Translation Networks Ming-Yu Liu, Thomas Breuel, Jan Kautz
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe Quentin Berthet, Vianney Perchet
Identifying Outlier Arms in Multi-Armed Bandit Honglei Zhuang, Chi Wang, Yifan Wang
Discovering Potential Correlations via Hypercontractivity Hyeji Kim, Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering Hongteng Xu, Hongyuan Zha
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification Muhammad Farhan, Juvaria Tariq, Arif Zaman, Mudassir Shabbir, Imdad Ullah Khan
Multi-output Polynomial Networks and Factorization Machines Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda
Tractability in Structured Probability Spaces Arthur Choi, Yujia Shen, Adnan Darwiche
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search Luigi Acerbi, Wei Ji Ma
Multi-Information Source Optimization Matthias Poloczek, Jialei Wang, Peter Frazier
Differentially private Bayesian learning on distributed data Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela
MMD GAN: Towards Deeper Understanding of Moment Matching Network Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabas Poczos
Convergence of Gradient EM on Multi-component Mixture of Gaussians Bowei Yan, Mingzhang Yin, Purnamrita Sarkar
Bayesian Dyadic Trees and Histograms for Regression Stéphanie van der Pas, Veronika Ročková
Efficient and Flexible Inference for Stochastic Systems Stefan Bauer, Nico S Gorbach, Djordje Miladinovic, Joachim M Buhmann
Learning ReLUs via Gradient Descent Mahdi Soltanolkotabi
Learning Graph Representations with Embedding Propagation Alberto Garcia Duran, Mathias Niepert
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation Matthias Hein, Maksym Andriushchenko
Collapsed variational Bayes for Markov jump processes Boqian Zhang, Jiangwei Pan, Vinayak A Rao
Is the Bellman residual a bad proxy? Matthieu Geist, Bilal Piot, Olivier Pietquin
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems Celestine Dünner, Thomas Parnell, Martin Jaggi
Noise-Tolerant Interactive Learning Using Pairwise Comparisons Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, Artur Dubrawski
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs Sanjiban Choudhury, Shervin Javdani, Siddhartha Srinivasa, Sebastian Scherer
Minimal Exploration in Structured Stochastic Bandits Richard Combes, Stefan Magureanu, Alexandre Proutiere
Learning Efficient Object Detection Models with Knowledge Distillation Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, Manmohan Chandraker
Learning Chordal Markov Networks via Branch and Bound Kari Rantanen, Antti Hyttinen, Matti Järvisalo
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding Wenbing Huang, Mehrtash Harandi, Tong Zhang, Lijie Fan, Fuchun Sun, Junzhou Huang
Deep Subspace Clustering Networks Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, Ian Reid
Robust Estimation of Neural Signals in Calcium Imaging Hakan Inan, Murat A Erdogdu, Mark Schnitzer
Fast-Slow Recurrent Neural Networks Asier Mujika, Florian Meier, Angelika Steger
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, Philip S Yu
Dual Discriminator Generative Adversarial Nets Tu Nguyen, Trung Le, Hung Vu, Dinh Phung
Beyond Parity: Fairness Objectives for Collaborative Filtering Sirui Yao, Bert Huang
Multitask Spectral Learning of Weighted Automata Guillaume Rabusseau, Borja Balle, Joelle Pineau
A simple neural network module for relational reasoning Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks Arash Vahdat
Stochastic Mirror Descent in Variationally Coherent Optimization Problems Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen Boyd, Peter W. Glynn
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication Qian Yu, Mohammad Maddah-Ali, Salman Avestimehr
From Bayesian Sparsity to Gated Recurrent Nets Hao He, Bo Xin, Satoshi Ikehata, David Wipf
Compatible Reward Inverse Reinforcement Learning Alberto Maria Metelli, Matteo Pirotta, Marcello Restelli
Consistent Robust Regression Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, Purushottam Kar
Scalable Variational Inference for Dynamical Systems Nico S Gorbach, Stefan Bauer, Joachim M Buhmann
Learning multiple visual domains with residual adapters Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi
Incorporating Side Information by Adaptive Convolution Di Kang, Debarun Dhar, Antoni Chan
Hierarchical Clustering Beyond the Worst-Case Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference Geoffrey Roeder, Yuhuai Wu, David K. Duvenaud
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos Gerasimos Palaiopanos, Ioannis Panageas, Georgios Piliouras
QMDP-Net: Deep Learning for Planning under Partial Observability Peter Karkus, David Hsu, Wee Sun Lee
Deep Supervised Discrete Hashing Qi Li, Zhenan Sun, Ran He, Tieniu Tan
Approximation Algorithms for $\ell_0$-Low Rank Approximation Karl Bringmann, Pavel Kolev, David Woodruff
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang
A Learning Error Analysis for Structured Prediction with Approximate Inference Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang
Simple strategies for recovering inner products from coarsely quantized random projections Ping Li, Martin Slawski
Trimmed Density Ratio Estimation Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu
Adaptive Batch Size for Safe Policy Gradients Matteo Papini, Matteo Pirotta, Marcello Restelli
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting Rebecca Morrison, Ricardo Baptista, Youssef Marzouk
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, Jascha Sohl-Dickstein
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues Noga Alon, Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran, Amir Yehudayoff
Tensor Biclustering Soheil Feizi, Hamid Javadi, David Tse
On the Model Shrinkage Effect of Gamma Process Edge Partition Models Iku Ohama, Issei Sato, Takuya Kida, Hiroki Arimura
Estimating Mutual Information for Discrete-Continuous Mixtures Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
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
An inner-loop free solution to inverse problems using deep neural networks Kai Fan, Qi Wei, Lawrence Carin, Katherine A. Heller
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control Fanny Yang, Aaditya Ramdas, Kevin G. Jamieson, Martin J. Wainwright
Interactive Submodular Bandit Lin Chen, Andreas Krause, Amin Karbasi
Hash Embeddings for Efficient Word Representations Dan Tito Svenstrup, Jonas Hansen, Ole Winther
Learning Low-Dimensional Metrics Blake Mason, Lalit Jain, Robert Nowak
Unsupervised Sequence Classification using Sequential Output Statistics Yu Liu, Jianshu Chen, Li Deng
Deep Sets Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander J Smola
Optimal Shrinkage of Singular Values Under Random Data Contamination Danny Barash, Matan Gavish
Learning Mixture of Gaussians with Streaming Data Aditi Raghunathan, Prateek Jain, Ravishankar Krishnawamy
Learning to Compose Domain-Specific Transformations for Data Augmentation Alexander J Ratner, Henry Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré
Preventing Gradient Explosions in Gated Recurrent Units Sekitoshi Kanai, Yasuhiro Fujiwara, Sotetsu Iwamura
Streaming Sparse Gaussian Process Approximations Thang D Bui, Cuong Nguyen, Richard E Turner
Differentially Private Empirical Risk Minimization Revisited: Faster and More General Di Wang, Minwei Ye, Jinhui Xu
Unbounded cache model for online language modeling with open vocabulary Edouard Grave, Moustapha M Cisse, Armand Joulin
Shape and Material from Sound Zhoutong Zhang, Qiujia Li, Zhengjia Huang, Jiajun Wu, Josh Tenenbaum, Bill Freeman
On the Consistency of Quick Shift Heinrich Jiang
Wasserstein Learning of Deep Generative Point Process Models Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Le Song, Hongyuan Zha
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, Julien Stainer
Protein Interface Prediction using Graph Convolutional Networks Alex Fout, Jonathon Byrd, Basir Shariat, Asa Ben-Hur
Convergence rates of a partition based Bayesian multivariate density estimation method Linxi Liu, Dangna Li, Wing Hung Wong
Avoiding Discrimination through Causal Reasoning Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
Alternating Estimation for Structured High-Dimensional Multi-Response Models Sheng Chen, Arindam Banerjee
Multimodal Learning and Reasoning for Visual Question Answering Ilija Ilievski, Jiashi Feng
Generative Local Metric Learning for Kernel Regression Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank Park, Daniel D Lee
Overcoming Catastrophic Forgetting by Incremental Moment Matching Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, Byoung-Tak Zhang
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, Ji Liu
Gradient Descent Can Take Exponential Time to Escape Saddle Points Simon S Du, Chi Jin, Jason Lee, Michael I Jordan, Aarti Singh, Barnabas Poczos
Dual Path Networks Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, Jiashi Feng
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
Universal consistency and minimax rates for online Mondrian Forests Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet
Gradient Episodic Memory for Continual Learning David Lopez-Paz, Marc'Aurelio Ranzato
Variational Inference for Gaussian Process Models with Linear Complexity Ching-An Cheng, Byron Boots
The Reversible Residual Network: Backpropagation Without Storing Activations Aidan N Gomez, Mengye Ren, Raquel Urtasun, Roger B Grosse
Language Modeling with Recurrent Highway Hypernetworks Joseph Suarez
Parametric Simplex Method for Sparse Learning Haotian Pang, Han Liu, Robert J Vanderbei, Tuo Zhao
Filtering Variational Objectives Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Teh
Cold-Start Reinforcement Learning with Softmax Policy Gradient Nan Ding, Radu Soricut
Bridging the Gap Between Value and Policy Based Reinforcement Learning Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans
Asynchronous Coordinate Descent under More Realistic Assumptions Tao Sun, Robert Hannah, Wotao Yin
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
Natural Value Approximators: Learning when to Trust Past Estimates Zhongwen Xu, Joseph Modayil, Hado P van Hasselt, Andre Barreto, David Silver, Tom Schaul
Active Exploration for Learning Symbolic Representations Garrett Andersen, George Konidaris
Balancing information exposure in social networks Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti
Nonlinear Acceleration of Stochastic Algorithms Damien Scieur, Francis Bach, Alexandre d'Aspremont
Multi-way Interacting Regression via Factorization Machines Mikhail Yurochkin, Xuanlong Nguyen, nikolaos Vasiloglou
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities Arun Suggala, Mladen Kolar, Pradeep K Ravikumar
Generating steganographic images via adversarial training Jamie Hayes, George Danezis
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features Fei Xia, Martin J Zhang, James Y Zou, David Tse
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis Tor Lattimore
Value Prediction Network Junhyuk Oh, Satinder Singh, Honglak Lee
Detrended Partial Cross Correlation for Brain Connectivity Analysis Jaime Ide, Fábio Cappabianco, Fabio Faria, Chiang-shan R Li

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