NeurIPS 2018

1,009 papers

Content preserving text generation with attribute controls

Lajanugen Logeswaran, Honglak Lee, Samy Bengio

Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling

Emilie Kaufmann, Wouter M. Koolen, Aurélien Garivier

NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

Marco Ciccone, Marco Gallieri, Jonathan Masci, Christian Osendorfer, Faustino Gomez

Query Complexity of Bayesian Private Learning

Kuang Xu

Densely Connected Attention Propagation for Reading Comprehension

Yi Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su

Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors

Fei Jiang, Guosheng Yin, Francesca Dominici

Faster Neural Networks Straight from JPEG

Lionel Gueguen, Alex Sergeev, Ben Kadlec, Rosanne Liu, Jason Yosinski

Streaming Kernel PCA with Õ(√n) Random Features

Enayat Ullah, Poorya Mianjy, Teodor Vanislavov Marinov, Raman Arora

Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction

Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum

PCA of high dimensional random walks with comparison to neural network training

Joseph Antognini, Jascha Sohl-dickstein

Iterative Value-Aware Model Learning

Amir-massoud Farahmand

Natasha 2: Faster Non-Convex Optimization Than SGD

Zeyuan Allen-Zhu

Distributionally Robust Graphical Models

Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart

Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation

Chaitanya Ryali, Gautam Reddy, Angela J. Yu

Evolved Policy Gradients

Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski

Learning and Testing Causal Models with Interventions

Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy

Revisiting (ε, γ, τ)-similarity learning for domain adaptation

Sofiane Dhouib, Ievgen Redko

The Limits of Post-Selection Generalization

Jonathan Ullman, Adam Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke

Discretely Relaxing Continuous Variables for tractable Variational Inference

Trefor Evans, Prasanth Nair

Improving Neural Program Synthesis with Inferred Execution Traces

Eui Chul Shin, Illia Polosukhin, Dawn Song

Automatic differentiation in ML: Where we are and where we should be going

Bart van Merrienboer, Olivier Breuleux, Arnaud Bergeron, Pascal Lamblin

Simple random search of static linear policies is competitive for reinforcement learning

Horia Mania, Aurelia Guy, Benjamin Recht

FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction

Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang

Maximizing Induced Cardinality Under a Determinantal Point Process

Jennifer A Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet

GILBO: One Metric to Measure Them All

Alexander A Alemi, Ian Fischer

Sparse PCA from Sparse Linear Regression

Guy Bresler, Sung Min Park, Madalina Persu

Learning Task Specifications from Demonstrations

Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K Ho, Sanjit Seshia

Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming

Bart van Merrienboer, Dan Moldovan, Alexander Wiltschko

Variational Bayesian Monte Carlo

Luigi Acerbi

Transfer of Deep Reactive Policies for MDP Planning

Aniket (Nick) Bajpai, Sankalp Garg, Mausam

Boosting Black Box Variational Inference

Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Raetsch

Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability

Michael Tsang, Hanpeng Liu, Sanjay Purushotham, Pavankumar Murali, Yan Liu

Boolean Decision Rules via Column Generation

Sanjeeb Dash, Oktay Gunluk, Dennis Wei

Learning with SGD and Random Features

Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco

Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, Ingmar Posner

Causal Inference via Kernel Deviance Measures

Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh

VideoCapsuleNet: A Simplified Network for Action Detection

Kevin Duarte, Yogesh Rawat, Mubarak Shah

Learning Latent Subspaces in Variational Autoencoders

Jack Klys, Jake Snell, Richard Zemel

Improving Online Algorithms via ML Predictions

Manish Purohit, Zoya Svitkina, Ravi Kumar

BRITS: Bidirectional Recurrent Imputation for Time Series

Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li

Overlapping Clustering Models, and One (class) SVM to Bind Them All

Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti

Supervising Unsupervised Learning

Vikas Garg, Adam T Kalai

Hybrid Knowledge Routed Modules for Large-scale Object Detection

ChenHan Jiang, Hang Xu, Xiaodan Liang, Liang Lin

Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces

Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama

Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization

Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu

Learning to Play With Intrinsically-Motivated, Self-Aware Agents

Nick Haber, Damian Mrowca, Stephanie Wang, Li F Fei-Fei, Daniel L Yamins

Deep Reinforcement Learning of Marked Temporal Point Processes

Utkarsh Upadhyay, Abir De, Manuel Gomez Rodriguez

To Trust Or Not To Trust A Classifier

Heinrich Jiang, Been Kim, Melody Guan, Maya Gupta

Adversarial Multiple Source Domain Adaptation

Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, Joao P. Costeira

Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions

Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang

Object-Oriented Dynamics Predictor

Guangxiang Zhu, Zhiao Huang, Chongjie Zhang

Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes

Hassan Ashtiani, Shai Ben-David, Nicholas Harvey, Christopher Liaw, Abbas Mehrabian

A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices

Rudrasis Chakraborty, Chun-Hao Yang, Xingjian Zhen, Monami Banerjee, Derek Archer

Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms

Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn

Image Inpainting via Generative Multi-column Convolutional Neural Networks

Yi Wang, Xin Tao, Xiaojuan Qi, Xiaoyong Shen, Jiaya Jia

Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior

Sid Reddy, Anca Dragan, Sergey Levine

Deep Neural Nets with Interpolating Function as Output Activation

Bao Wang, Xiyang Luo, Zhen Li, Wei Zhu, Zuoqiang Shi

Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization

Francis Bach

Quadrature-based features for kernel approximation

Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan Oseledets

Learning Attractor Dynamics for Generative Memory

Yan Wu, Gregory Wayne, Karol Gregor, Timothy Lillicrap

A Simple Cache Model for Image Recognition

Emin Orhan

Paraphrasing Complex Network: Network Compression via Factor Transfer

Jangho Kim, Seonguk Park, Nojun Kwak

Entropy and mutual information in models of deep neural networks

Marylou Gabrie, Andre Manoel, Clément Luneau, jean barbier, Nicolas Macris

Hunting for Discriminatory Proxies in Linear Regression Models

Samuel Yeom, Anupam Datta, Matt Fredrikson

Regret Bounds for Online Portfolio Selection with a Cardinality Constraint

Shinji Ito, Daisuke Hatano, Hanna Sumita, Akihiro Yabe, Takuro Fukunaga

Contrastive Learning from Pairwise Measurements

Yi Chen, Zhuoran Yang, Yuchen Xie, Zhaoran Wang

Task-Driven Convolutional Recurrent Models of the Visual System

Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli

Policy Optimization via Importance Sampling

Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli

Multi-domain Causal Structure Learning in Linear Systems

AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang

Coupled Variational Bayes via Optimization Embedding

Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu

Training DNNs with Hybrid Block Floating Point

Mario Drumond, Tao LIN, Martin Jaggi, Babak Falsafi

Speaker-Follower Models for Vision-and-Language Navigation

Daniel Fried, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas

Learning Attentional Communication for Multi-Agent Cooperation

Jiechuan Jiang, Zongqing Lu

Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images

Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu

Gaussian Process Conditional Density Estimation

Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Deisenroth

On Controllable Sparse Alternatives to Softmax

Anirban Laha, Saneem Ahmed Chemmengath, Priyanka Agrawal, Mitesh Khapra, Karthik Sankaranarayanan

ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions

Hongyang Gao, Zhengyang Wang, Shuiwang Ji

MetaAnchor: Learning to Detect Objects with Customized Anchors

Tong Yang, Xiangyu Zhang, Zeming Li, Wenqiang Zhang, Jian Sun

Neural Ordinary Differential Equations

Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David K. Duvenaud

Universal Growth in Production Economies

Simina Branzei, Ruta Mehta, Noam Nisan

RenderNet: A deep convolutional network for differentiable rendering from 3D shapes

Thu H Nguyen-Phuoc, Chuan Li, Stephen Balaban, Yongliang Yang

Algorithmic Linearly Constrained Gaussian Processes

Markus Lange-Hegermann

Learning to Repair Software Vulnerabilities with Generative Adversarial Networks

Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher Reale, Rebecca Russell

Learning to Solve SMT Formulas

Mislav Balunovic, Pavol Bielik, Martin Vechev

Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

Ye Jia, Yu Zhang, Ron Weiss, Quan Wang, Jonathan Shen

Variational PDEs for Acceleration on Manifolds and Application to Diffeomorphisms

Ganesh Sundaramoorthi, Anthony Yezzi

New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity

Pan Zhou, Xiaotong Yuan, Jiashi Feng

Overcoming Language Priors in Visual Question Answering with Adversarial Regularization

Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee

Neural Guided Constraint Logic Programming for Program Synthesis

Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd

Learning to Infer Graphics Programs from Hand-Drawn Images

Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, Josh Tenenbaum

A Bayes-Sard Cubature Method

Toni Karvonen, Chris J Oates, Simo Särkkä

TADAM: Task dependent adaptive metric for improved few-shot learning

Boris Oreshkin, Pau Rodríguez López, Alexandre Lacoste

How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective

Lei Wu, Chao Ma, Weinan E

Representation Learning of Compositional Data

Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun

Visual Memory for Robust Path Following

Ashish Kumar, Saurabh Gupta, David Fouhey, Sergey Levine, Jitendra Malik

Balanced Policy Evaluation and Learning

Nathan Kallus

Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks

Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi

Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs

Yanlin Han, Piotr Gmytrasiewicz

Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization

Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang

Learning Loop Invariants for Program Verification

Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song