NeurIPS 2018

1,009 papers

Multi-Task Zipping via Layer-wise Neuron Sharing

Xiaoxi He, Zimu Zhou, Lothar Thiele

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel

Online Improper Learning with an Approximation Oracle

Elad Hazan, Wei Hu, Yuanzhi Li, Zhiyuan Li

Theoretical guarantees for EM under misspecified Gaussian mixture models

Raaz Dwivedi, nhật Hồ, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan

Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems

Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P. Xing

Differential Privacy for Growing Databases

Rachel Cummings, Sara Krehbiel, Kevin A Lai, Uthaipon Tantipongpipat

Breaking the Span Assumption Yields Fast Finite-Sum Minimization

Robert Hannah, Yanli Liu, Daniel O'Connor, Wotao Yin

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin

Bayesian Nonparametric Spectral Estimation

Felipe Tobar

Stochastic Expectation Maximization with Variance Reduction

Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang

Conditional Adversarial Domain Adaptation

Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, Michael I. Jordan, Michael I Jordan

Bipartite Stochastic Block Models with Tiny Clusters

Stefan Neumann

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain

Towards Text Generation with Adversarially Learned Neural Outlines

Sandeep Subramanian, Sai Rajeswar Mudumba, Alessandro Sordoni, Adam Trischler, Aaron C. Courville

Model-Agnostic Private Learning

Raef Bassily, Om Thakkar, Abhradeep Guha Thakurta

A Unified Framework for Extensive-Form Game Abstraction with Bounds

Christian Kroer, Tuomas Sandholm

Testing for Families of Distributions via the Fourier Transform

Clément L Canonne, Ilias Diakonikolas, Alistair Stewart

Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems

Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi

Frequency-Domain Dynamic Pruning for Convolutional Neural Networks

Zhenhua Liu, Jizheng Xu, Xiulian Peng, Ruiqin Xiong

A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers

Omer Ben-Porat, Moshe Tennenholtz

Evidential Deep Learning to Quantify Classification Uncertainty

Murat Sensoy, Lance Kaplan, Melih Kandemir

Differentially Private Robust Low-Rank Approximation

Raman Arora, Vladimir braverman, Jalaj Upadhyay

Wenye Li, Jingwei Mao, Yin Zhang, Shuguang Cui

Learning Safe Policies with Expert Guidance

Jessie Huang, Fa Wu, Doina Precup, Yang Cai

Efficient nonmyopic batch active search

Shali Jiang, Gustavo Malkomes, Matthew Abbott, Benjamin Moseley, Roman Garnett

Insights on representational similarity in neural networks with canonical correlation

Ari Morcos, Maithra Raghu, Samy Bengio

Data Amplification: A Unified and Competitive Approach to Property Estimation

Yi Hao, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu

DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning

Runsheng Yu, Wenyu Liu, Yasen Zhang, Zhi Qu, Deli Zhao

HOGWILD!-Gibbs can be PanAccurate

Constantinos Daskalakis, Nishanth Dikkala, Siddhartha Jayanti

Memory Replay GANs: Learning to Generate New Categories without Forgetting

Chenshen Wu, Luis Herranz, Xialei Liu, yaxing wang, Joost van de Weijer

Representation Learning for Treatment Effect Estimation from Observational Data

Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao

Non-monotone Submodular Maximization in Exponentially Fewer Iterations

Eric Balkanski, Adam Breuer, Yaron Singer

Visualizing the Loss Landscape of Neural Nets

Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein

Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network

Risi Kondor, Zhen Lin, Shubhendu Trivedi

Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergences

Jeremias Knoblauch, Jack E Jewson, Theodoros Damoulas

Information-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces

Boyla Mainsah, Dmitry Kalika, Leslie Collins, Siyuan Liu, Chandra Throckmorton

Mallows Models for Top-k Lists

Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi

Training Neural Networks Using Features Replay

Zhouyuan Huo, Bin Gu, Heng Huang

Deep Structured Prediction with Nonlinear Output Transformations

Colin Graber, Ofer Meshi, Alexander Schwing

GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking

Patrick Chen, Si Si, Yang Li, Ciprian Chelba, Cho-jui Hsieh

On Neuronal Capacity

Pierre Baldi, Roman Vershynin

Diversity-Driven Exploration Strategy for Deep Reinforcement Learning

Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu

Learning to Reason with Third Order Tensor Products

Imanol Schlag, Jürgen Schmidhuber

Differentially Private Contextual Linear Bandits

Roshan Shariff, Or Sheffet

Smoothed analysis of the low-rank approach for smooth semidefinite programs

Thomas Pumir, Samy Jelassi, Nicolas Boumal

On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport

Lénaïc Chizat, Francis Bach

Co-teaching: Robust training of deep neural networks with extremely noisy labels

Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu

Predictive Approximate Bayesian Computation via Saddle Points

Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He

Computationally and statistically efficient learning of causal Bayes nets using path queries

Kevin Bello, Jean Honorio

Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies

Alessandro Achille, Tom Eccles, Loic Matthey, Chris Burgess, Nicholas Watters

Robust Hypothesis Testing Using Wasserstein Uncertainty Sets

RUI GAO, Liyan Xie, Yao Xie, Huan Xu

Byzantine Stochastic Gradient Descent

Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li

Dialog-based Interactive Image Retrieval

Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro

Local Differential Privacy for Evolving Data

Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner

Norm matters: efficient and accurate normalization schemes in deep networks

Elad Hoffer, Ron Banner, Itay Golan, Daniel Soudry

Adding One Neuron Can Eliminate All Bad Local Minima

SHIYU LIANG, Ruoyu Sun, Jason Lee, R. Srikant

GumBolt: Extending Gumbel trick to Boltzmann priors

Amir H Khoshaman, Mohammad Amin

Reinforcement Learning for Solving the Vehicle Routing Problem

MohammadReza Nazari, Afshin Oroojlooy, Lawrence Snyder, Martin Takac

DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors

Arash Vahdat, Evgeny Andriyash, William Macready

Adaptive Learning with Unknown Information Flows

Yonatan Gur, Ahmadreza Momeni

Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning

Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu, Qi Wu

Sigsoftmax: Reanalysis of the Softmax Bottleneck

Sekitoshi Kanai, Yasuhiro Fujiwara, Yuki Yamanaka, Shuichi Adachi

Stimulus domain transfer in recurrent models for large scale cortical population prediction on video

Fabian Sinz, Alexander S Ecker, Paul Fahey, Edgar Walker, Erick Cobos

Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance

Neal Jean, Sang Michael Xie, Stefano Ermon

Online convex optimization for cumulative constraints

Jianjun Yuan, Andrew Lamperski

Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu

Size-Noise Tradeoffs in Generative Networks

Bolton Bailey, Matus J. Telgarsky, Matus J Telgarsky

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language

Matthew D. Hoffman, Matthew J Johnson, Dustin Tran

On Misinformation Containment in Online Social Networks

Amo Tong, Ding-Zhu Du, Weili Wu

Information Constraints on Auto-Encoding Variational Bayes

Romain Lopez, Jeffrey Regier, Nir Yosef, Michael I. Jordan, Michael I Jordan

Unsupervised Depth Estimation, 3D Face Rotation and Replacement

Joel Ruben Antony Moniz, Christopher Beckham, Simon Rajotte, Sina Honari, Chris Pal

Data-dependent PAC-Bayes priors via differential privacy

Gintare Karolina Dziugaite, Daniel M. Roy

Entropy Rate Estimation for Markov Chains with Large State Space

Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu

How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD

Zeyuan Allen-Zhu

Co-regularized Alignment for Unsupervised Domain Adaptation

Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris

Pelee: A Real-Time Object Detection System on Mobile Devices

Robert J. Wang, Xiang Li, Charles X. Ling

Generalisation of structural knowledge in the hippocampal-entorhinal system

James Whittington, Timothy Muller, Shirely Mark, Caswell Barry, Tim Behrens

L4: Practical loss-based stepsize adaptation for deep learning

Michal Rolinek, Georg Martius

See and Think: Disentangling Semantic Scene Completion

Shice Liu, YU HU, Yiming Zeng, Qiankun Tang, Beibei Jin

Representation Balancing MDPs for Off-policy Policy Evaluation

Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A Faisal

Learning semantic similarity in a continuous space

Michel Deudon

Online Reciprocal Recommendation with Theoretical Performance Guarantees

Fabio Vitale, Nikos Parotsidis, Claudio Gentile

Turbo Learning for CaptionBot and DrawingBot

Qiuyuan Huang, Pengchuan Zhang, Dapeng Wu, Lei Zhang

Improving Simple Models with Confidence Profiles

Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen

Constructing Fast Network through Deconstruction of Convolution

Yunho Jeon, Junmo Kim

Unsupervised Video Object Segmentation for Deep Reinforcement Learning

Vikash Goel, Jameson Weng, Pascal Poupart

Bilinear Attention Networks

Jin-Hwa Kim, Jaehyun Jun, Byoung-tak Zhang

An Efficient Pruning Algorithm for Robust Isotonic Regression

Cong Han Lim

Multivariate Time Series Imputation with Generative Adversarial Networks

Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, Yuan xiaojie

Learning Beam Search Policies via Imitation Learning

Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon

Training Deep Models Faster with Robust, Approximate Importance Sampling

Tyler B Johnson, Carlos Guestrin

Constructing Deep Neural Networks by Bayesian Network Structure Learning

Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik

Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams

Tam Le, Makoto Yamada

A loss framework for calibrated anomaly detection

Flexible and accurate inference and learning for deep generative models

Eszter Vértes, Maneesh Sahani

Knowledge Distillation by On-the-Fly Native Ensemble

xu lan, Xiatian Zhu, Shaogang Gong

Credit Assignment For Collective Multiagent RL With Global Rewards

Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

A Bayesian Approach to Generative Adversarial Imitation Learning

Wonseok Jeon, Seokin Seo, Kee-eung Kim

Learning Optimal Reserve Price against Non-myopic Bidders

Jinyan Liu, Zhiyi Huang, Xiangning Wang

MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare

Edward Choi, Cao Xiao, Walter Stewart, Jimeng Sun