NeurIPS 2019

1,428 papers

Visualizing and Measuring the Geometry of BERT

Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B Viegas, Andy Coenen

Online Continual Learning with Maximal Interfered Retrieval

Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia

Sequence Modeling with Unconstrained Generation Order

Dmitrii Emelianenko, Elena Voita, Pavel Serdyukov

Bayesian Layers: A Module for Neural Network Uncertainty

Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner

Poisson-Randomized Gamma Dynamical Systems

Aaron Schein, Scott Linderman, Mingyuan Zhou, David Blei, Hanna Wallach

A Bayesian Theory of Conformity in Collective Decision Making

Koosha Khalvati, Saghar Mirbagheri, Seongmin A. Park, Jean-Claude Dreher, Rajesh P. Rao

Dying Experts: Efficient Algorithms with Optimal Regret Bounds

Hamid Shayestehmanesh, Sajjad Azami, Nishant A Mehta

Global Convergence of Gradient Descent for Deep Linear Residual Networks

Lei Wu, Qingcan Wang, Chao Ma

Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

Sai Qian Zhang, Qi Zhang, Jieyu Lin

Learning dynamic polynomial proofs

Alhussein Fawzi, Mateusz Malinowski, Hamza Fawzi, Omar Fawzi

On Distributed Averaging for Stochastic k-PCA

Aditya Bhaskara, Pruthuvi Maheshakya Wijewardena

Learning Sparse Distributions using Iterative Hard Thresholding

Jacky Y Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi O Koyejo

Joint-task Self-supervised Learning for Temporal Correspondence

Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz

Probabilistic Logic Neural Networks for Reasoning

Meng Qu, Jian Tang

Statistical-Computational Tradeoff in Single Index Models

Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

Learning Representations for Time Series Clustering

Qianli Ma, Jiawei Zheng, Sen Li, Gary W Cottrell

A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment

Felix Leibfried, Sergio Pascual-Díaz, Jordi Grau-moya

Learning to Perform Local Rewriting for Combinatorial Optimization

Xinyun Chen, Yuandong Tian

Learning Local Search Heuristics for Boolean Satisfiability

Emre Yolcu, Barnabás Póczos

PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph

Yikang LI, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei

Learning metrics for persistence-based summaries and applications for graph classification

Qi Zhao, Yusu Wang

Efficiently avoiding saddle points with zero order methods: No gradients required

Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras

Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias

Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna

Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees

Alix LHERITIER, Frederic Cazals

CPM-Nets: Cross Partial Multi-View Networks

Changqing Zhang, Zongbo Han, yajie cui, Huazhu Fu, Joey Tianyi Zhou

A Simple Baseline for Bayesian Uncertainty in Deep Learning

Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, Andrew Gordon Wilson

Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes

James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E Turner

Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski

Sequential Neural Processes

Gautam Singh, Jaesik Yoon, Youngsung Son, Sungjin Ahn

Discovery of Useful Questions as Auxiliary Tasks

Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis

Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs

Max Simchowitz, Kevin G. Jamieson

In-Place Zero-Space Memory Protection for CNN

Hui Guan, Lin Ning, Zhen Lin, Xipeng Shen, Huiyang Zhou

On Robustness to Adversarial Examples and Polynomial Optimization

Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan

Learning Fairness in Multi-Agent Systems

Jiechuan Jiang, Zongqing Lu

Hyperbolic Graph Neural Networks

Qi Liu, Maximilian Nickel, Douwe Kiela

The Label Complexity of Active Learning from Observational Data

Songbai Yan, Kamalika Chaudhuri, Tara Javidi

Möbius Transformation for Fast Inner Product Search on Graph

Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, Ping Li

Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery

Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell

Minimizers of the Empirical Risk and Risk Monotonicity

Marco Loog, Tom Viering, Alexander Mey

On the Convergence Rate of Training Recurrent Neural Networks

Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song

Training Image Estimators without Image Ground Truth

Zhihao Xia, Ayan Chakrabarti

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders

Emile Mathieu, Charline Le Lan, Ryota Tomioka, Yee Whye Teh, Chris J Maddison

Adaptive Auxiliary Task Weighting for Reinforcement Learning

Xingyu Lin, Harjatin Baweja, George Kantor, David Held

Semi-supervisedly Co-embedding Attributed Networks

Zaiqiao Meng, Shangsong Liang, Jinyuan Fang, Teng Xiao

Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation

Colin Wei, Tengyu Ma

Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse

Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens

Nonstochastic Multiarmed Bandits with Unrestricted Delays

Tobias Sommer Thune, Nicolò Cesa-bianchi, Yevgeny Seldin

Comparing distributions: ℓ_1 geometry improves kernel two-sample testing

meyer scetbon, Gael Varoquaux

Average Individual Fairness: Algorithms, Generalization and Experiments

Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth

Generalized Off-Policy Actor-Critic

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson

Adversarial Robustness through Local Linearization

Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham

Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction

Alban Laflaquière, Michael Garcia Ortiz

Efficient Pure Exploration in Adaptive Round model

Tianyuan Jin, Jieming SHI, Xiaokui Xiao, Enhong Chen

Retrosynthesis Prediction with Conditional Graph Logic Network

Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak

Fast Low-rank Metric Learning for Large-scale and High-dimensional Data

Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu

Localized Structured Prediction

Carlo Ciliberto, Francis Bach, Alessandro Rudi

Combinatorial Inference against Label Noise

Paul Hongsuck Seo, Geeho Kim, Bohyung Han

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang

Deep Gamblers: Learning to Abstain with Portfolio Theory

Ziyin Liu, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency

Optimal Sketching for Kronecker Product Regression and Low Rank Approximation

Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David Woodruff

Learning Macroscopic Brain Connectomes via Group-Sparse Factorization

Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell

High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes

David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus

Language as an Abstraction for Hierarchical Deep Reinforcement Learning

YiDing Jiang, Shixiang (Shane) Gu, Kevin P. Murphy, Chelsea Finn

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

Yuge Shi, Siddharth N, Brooks Paige, Philip Torr

Beyond the Single Neuron Convex Barrier for Neural Network Certification

Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin Vechev

Learning Stable Deep Dynamics Models

J. Zico Kolter, Gaurav Manek

Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing

Jonas W Mueller, Vasilis Syrgkanis, Matt Taddy

Implicitly learning to reason in first-order logic

Vaishak Belle, Brendan Juba

Power analysis of knockoff filters for correlated designs

Jingbo Liu, Philippe Rigollet

Linear Stochastic Bandits Under Safety Constraints

Sanae Amani, Mahnoosh Alizadeh, CHRISTOS THRAMPOULIDIS

DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node

Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnawamy, Rohan Kadekodi

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen

Coda: An End-to-End Neural Program Decompiler

Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian

Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem

DongDong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye

Concentration of risk measures: A Wasserstein distance approach

Sanjay P. Bhat, Prashanth L.A.

Semi-Parametric Efficient Policy Learning with Continuous Actions

Victor Chernozhukov, Mert Demirer, Greg Lewis, Vasilis Syrgkanis

Equal Opportunity in Online Classification with Partial Feedback

Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Z. Wu

Visual Sequence Learning in Hierarchical Prediction Networks and Primate Visual Cortex

The Geometry of Deep Networks: Power Diagram Subdivision

Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard Baraniuk

A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits

Wenhao Zhang, Si Wu, Brent Doiron, Tai Sing Lee

LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning

Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai

Universal Boosting Variational Inference

Trevor Campbell, Xinglong Li

Differentially Private Markov Chain Monte Carlo

Mikko Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela

Meta-Learning with Implicit Gradients

Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine

Reliable training and estimation of variance networks

Nicki Skafte, Martin Jørgensen, Søren Hauberg

State Aggregation Learning from Markov Transition Data

Yaqi Duan, Tracy Ke, Mengdi Wang

Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor

Meera Pai, Animesh Kumar

Bayesian Optimization under Heavy-tailed Payoffs

Sayak Ray Chowdhury, Aditya Gopalan

Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals

Surbhi Goel, Sushrut Karmalkar, Adam Klivans

Meta-Surrogate Benchmarking for Hyperparameter Optimization

Aaron Klein, Zhenwen Dai, Frank Hutter, Neil Lawrence, Javier Gonzalez

Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections

Raanan Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik

Robust exploration in linear quadratic reinforcement learning

Jack Umenberger, Mina Ferizbegovic, Thomas B Schön, Håkan Hjalmarsson

First order expansion of convex regularized estimators

Pierre Bellec, Arun Kuchibhotla

Uniform convergence may be unable to explain generalization in deep learning

Vaishnavh Nagarajan, J. Zico Kolter

Efficient Algorithms for Smooth Minimax Optimization

Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates

Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy

Capacity Bounded Differential Privacy

Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul Whatmough

Random Tessellation Forests

Shufei Ge, Shijia Wang, Yee Whye Teh, Liangliang Wang, Lloyd Elliott