NeurIPS 2019

1,428 papers

Flow-based Image-to-Image Translation with Feature Disentanglement

Ruho Kondo, Keisuke Kawano, Satoshi Koide, Takuro Kutsuna

Efficient Rematerialization for Deep Networks

Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua Wang

Fully Dynamic Consistent Facility Location

Vincent Cohen-Addad, Niklas Oskar D Hjuler, Nikos Parotsidis, David Saulpic, Chris Schwiegelshohn

Adaptively Aligned Image Captioning via Adaptive Attention Time

Lun Huang, Wenmin Wang, Yaxian Xia, Jie Chen

Discrete Object Generation with Reversible Inductive Construction

Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams

Random deep neural networks are biased towards simple functions

Giacomo De Palma, Bobak Kiani, Seth Lloyd

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Yunfei Teng, Wenbo Gao, François Chalus, Anna E Choromanska, Donald Goldfarb

Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control

Armin Lederer, Jonas Umlauft, Sandra Hirche

Zero-shot Knowledge Transfer via Adversarial Belief Matching

Paul Micaelli, Amos J. Storkey

Biases for Emergent Communication in Multi-agent Reinforcement Learning

Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel

A Benchmark for Interpretability Methods in Deep Neural Networks

Sara Hooker, Dumitru Erhan, Pieter-jan Kindermans, Been Kim

Multivariate Distributionally Robust Convex Regression under Absolute Error Loss

Jose Blanchet, Peter W. Glynn, Jun Yan, Zhengqing Zhou

GOT: An Optimal Transport framework for Graph comparison

Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds

Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao

Semi-Implicit Graph Variational Auto-Encoders

Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou

Transferable Normalization: Towards Improving Transferability of Deep Neural Networks

Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, Michael I. Jordan

Unsupervised Meta-Learning for Few-Shot Image Classification

Siavash Khodadadeh, Ladislau Boloni, Mubarak Shah

Classification Accuracy Score for Conditional Generative Models

Suman Ravuri, Oriol Vinyals

Computational Separations between Sampling and Optimization

Kunal Talwar

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering

Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Clark Glymour

Stagewise Training Accelerates Convergence of Testing Error Over SGD

Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang

On Tractable Computation of Expected Predictions

Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck

Robust Multi-agent Counterfactual Prediction

Alexander Peysakhovich, Christian Kroer, Adam Lerer

Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems

Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques, Craig Ferguson, Noah Jones

Counting the Optimal Solutions in Graphical Models

Radu Marinescu, Rina Dechter

The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers

Alex Lu, Amy Lu, Wiebke Schormann, Marzyeh Ghassemi, David Andrews

Fair Algorithms for Clustering

Suman Bera, Deeparnab Chakrabarty, Nicolas Flores, Maryam Negahbani

Differential Privacy Has Disparate Impact on Model Accuracy

Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov

Online Convex Matrix Factorization with Representative Regions

Jianhao Peng, Olgica Milenkovic, Abhishek Agarwal

Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models

Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Lenka Zdeborová

Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models

Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama

Blended Matching Pursuit

Cyrille Combettes, Sebastian Pokutta

Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses

Ananya Uppal, Shashank Singh, Barnabás Póczos

Primal-Dual Block Generalized Frank-Wolfe

Qi Lei, JIACHENG ZHUO, Constantine Caramanis, Inderjit S Dhillon, Alexandros G Dimakis

On the Downstream Performance of Compressed Word Embeddings

Avner May, Jian Zhang, Tri Dao, Christopher Re

KNG: The K-Norm Gradient Mechanism

Matthew Reimherr, Jordan Awan

Efficient online learning with kernels for adversarial large scale problems

Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi

Experience Replay for Continual Learning

David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, Gregory Wayne

Neural Diffusion Distance for Image Segmentation

Jian Sun, Zongben Xu

Exact sampling of determinantal point processes with sublinear time preprocessing

Michal Derezinski, Daniele Calandriello, Michal Valko

Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers

Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi Jaakkola

The Broad Optimality of Profile Maximum Likelihood

Yi Hao, Alon Orlitsky

Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections

Boris Muzellec, Marco Cuturi

Multi-Agent Common Knowledge Reinforcement Learning

Christian Schroeder de Witt, Jakob Foerster, Gregory Farquhar, Philip Torr, Wendelin Boehmer

Wasserstein Dependency Measure for Representation Learning

Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine

Differentially Private Bayesian Linear Regression

Garrett Bernstein, Daniel R. Sheldon

Normalization Helps Training of Quantized LSTM

Lu Hou, Jinhua Zhu, James Kwok, Fei Gao, Tao Qin

MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization

Shangyu Chen, Wenya Wang, Sinno Jialin Pan

Episodic Memory in Lifelong Language Learning

Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama

Verified Uncertainty Calibration

Ananya Kumar, Percy Liang, Tengyu Ma

Multi-relational Poincaré Graph Embeddings

Ivana Balazevic, Carl Allen, Timothy Hospedales

Multiclass Learning from Contradictions

Sauptik Dhar, Vladimir Cherkassky, Mohak Shah

DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters

W. O. K. Asiri Suranga Wijesinghe, Qing Wang

Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters

Alberto Maria Metelli, Amarildo Likmeta, Marcello Restelli

The continuous Bernoulli: fixing a pervasive error in variational autoencoders

Gabriel Loaiza-Ganem, John P. Cunningham

MAVEN: Multi-Agent Variational Exploration

Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson

Constrained deep neural network architecture search for IoT devices accounting for hardware calibration

Florian Scheidegger, Luca Benini, Costas Bekas, A. Cristiano I. Malossi

Certifying Geometric Robustness of Neural Networks

Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin Vechev

Variational Bayes under Model Misspecification

Yixin Wang, David Blei

High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks

Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V Le

On Adversarial Mixup Resynthesis

Christopher Beckham, Sina Honari, Vikas Verma, Alex M Lamb, Farnoosh Ghadiri

Compression with Flows via Local Bits-Back Coding

Jonathan Ho, Evan Lohn, Pieter Abbeel

Adaptive Influence Maximization with Myopic Feedback

Binghui Peng, Wei Chen

Park: An Open Platform for Learning-Augmented Computer Systems

Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang

Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards

Falcon Dai, Matthew Walter

FastSpeech: Fast, Robust and Controllable Text to Speech

Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao

On the Utility of Learning about Humans for Human-AI Coordination

Micah Carroll, Rohin Shah, Mark K Ho, Tom Griffiths, Sanjit Seshia

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer

Wenzheng Chen, huan ling, Jun Gao, Edward Smith, Jaakko Lehtinen

A Composable Specification Language for Reinforcement Learning Tasks

Kishor Jothimurugan, Rajeev Alur, Osbert Bastani

Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling

Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, Qibin Zhao

Massively scalable Sinkhorn distances via the Nyström method

Jason Altschuler, Francis Bach, Alessandro Rudi, Jonathan Niles-Weed

Meta-Learning Representations for Continual Learning

Khurram Javed, Martha White

Compositional generalization through meta sequence-to-sequence learning

Brenden M Lake

Adaptive Gradient-Based Meta-Learning Methods

Mikhail Khodak, Maria-Florina F Balcan, Ameet S Talwalkar

Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity

Aria Wang, Michael Tarr, Leila Wehbe

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks

Wenrui Zhang, Peng Li

Generative Models for Graph-Based Protein Design

John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola

Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks

Gauthier Gidel, Francis Bach, Simon Lacoste-Julien

Deep Learning without Weight Transport

Mohamed Akrout, Collin Wilson, Peter Humphreys, Timothy Lillicrap, Douglas B Tweed

Quantum Wasserstein Generative Adversarial Networks

Shouvanik Chakrabarti, Huang Yiming, Tongyang Li, Soheil Feizi, Xiaodi Wu

Thompson Sampling and Approximate Inference

My Phan, Yasin Abbasi Yadkori, Justin Domke

Global Sparse Momentum SGD for Pruning Very Deep Neural Networks

Xiaohan Ding, guiguang ding, Xiangxin Zhou, Yuchen Guo, Jungong Han

Nonzero-sum Adversarial Hypothesis Testing Games

Sarath Yasodharan, Patrick Loiseau

DppNet: Approximating Determinantal Point Processes with Deep Networks

Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek

Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning

Igor Colin, Ludovic DOS SANTOS, Kevin Scaman

Neural Attribution for Semantic Bug-Localization in Student Programs

Rahul Gupta, Aditya Kanade, Shirish Shevade

Towards a Zero-One Law for Column Subset Selection

Zhao Song, David Woodruff, Peilin Zhong

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

Brandon Yang, Gabriel Bender, Quoc V Le, Jiquan Ngiam

Deep Generative Video Compression

Salvator Lombardo, JUN HAN, Christopher Schroers, Stephan Mandt

A General Framework for Symmetric Property Estimation

Moses Charikar, Kirankumar Shiragur, Aaron Sidford

When does label smoothing help?

Rafael Müller, Simon Kornblith, Geoffrey E. Hinton

Prior-Free Dynamic Auctions with Low Regret Buyers

Yuan Deng, Jon Schneider, Balasubramanian Sivan

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference

Cole Hurwitz, Kai Xu, Akash Srivastava, Alessio Buccino, Matthias Hennig

Outlier-robust estimation of a sparse linear model using ℓ_1-penalized Huber's M-estimator

Arnak Dalalyan, Philip Thompson

Generalized Sliced Wasserstein Distances

Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo Rohde

Differentially Private Anonymized Histograms

Ananda Theertha Suresh

Deep Scale-spaces: Equivariance Over Scale

Daniel Worrall, Max Welling

Abstract Reasoning with Distracting Features

Kecheng Zheng, Zheng-Jun Zha, Wei Wei

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus