NeurIPS 2017

679 papers

QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic

Masked Autoregressive Flow for Density Estimation

George Papamakarios, Theo Pavlakou, Iain Murray

Stochastic and Adversarial Online Learning without Hyperparameters

Ashok Cutkosky, Kwabena A. Boahen

Convolutional Phase Retrieval

Qing Qu, Yuqian Zhang, Yonina Eldar, John Wright

Learning Overcomplete HMMs

Vatsal Sharan, Sham M. Kakade, Percy Liang, Gregory Valiant

Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes

Ahmed M. Alaa, Mihaela van der Schaar

Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks

Ziming Zhang, Matthew Brand

Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?

Cameron Musco, David Woodruff

Bayesian Compression for Deep Learning

Christos Louizos, Karen Ullrich, Max Welling

Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning

Liangpeng Zhang, Ke Tang, Xin Yao

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Antti Tarvainen, Harri Valpola

Testing and Learning on Distributions with Symmetric Noise Invariance

Ho Chung Law, Christopher Yau, Dino Sejdinovic

Attentional Pooling for Action Recognition

Rohit Girdhar, Deva Ramanan

Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation

Zhaohan Guo, Philip S. Thomas, Emma Brunskill

Straggler Mitigation in Distributed Optimization Through Data Encoding

Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin

Visual Reference Resolution using Attention Memory for Visual Dialog

Paul Hongsuck Seo, Andreas Lehrmann, Bohyung Han, Leonid Sigal

Bayesian Optimization with Gradients

Jian Wu, Matthias Poloczek, Andrew G Wilson, Peter Frazier

The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process

Hongyuan Mei, Jason M Eisner

LightGBM: A Highly Efficient Gradient Boosting Decision Tree

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen

Acceleration and Averaging in Stochastic Descent Dynamics

Walid Krichene, Peter L. Bartlett, Peter L Bartlett

On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks

Arturs Backurs, Piotr Indyk, Ludwig Schmidt

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

Francesco Locatello, Michael Tschannen, Gunnar Raetsch, Martin Jaggi

Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets

Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, Joseph J. Lim

Gaussian Quadrature for Kernel Features

Tri Dao, Christopher M De Sa, Christopher Re

Practical Hash Functions for Similarity Estimation and Dimensionality Reduction

Søren Dahlgaard, Mathias Knudsen, Mikkel Thorup

Learning Causal Structures Using Regression Invariance

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang

Federated Multi-Task Learning

Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet S Talwalkar

Predictive-State Decoders: Encoding the Future into Recurrent Networks

Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert

Generalization Properties of Learning with Random Features

Alessandro Rudi, Lorenzo Rosasco

Improved Graph Laplacian via Geometric Self-Consistency

Dominique Joncas, Marina Meila, James McQueen

SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud

Zahra Ghodsi, Tianyu Gu, Siddharth Garg

Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems

Ingmar Kanitscheider, Ila Fiete

Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions

Ryan J Tibshirani

Coded Distributed Computing for Inverse Problems

Yaoqing Yang, Pulkit Grover, Soummya Kar

Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs

Rowan McAllister, Carl Edward Rasmussen

Inductive Representation Learning on Large Graphs

Will Hamilton, Zhitao Ying, Jure Leskovec

Asynchronous Parallel Coordinate Minimization for MAP Inference

Ofer Meshi, Alexander Schwing

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

Self-Normalizing Neural Networks

Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter

Cross-Spectral Factor Analysis

Neil Gallagher, Kyle R Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin

A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening

Kevin Lin, James L Sharpnack, Alessandro Rinaldo, Ryan J Tibshirani

Variance-based Regularization with Convex Objectives

Hongseok Namkoong, John C. Duchi

Poincaré Embeddings for Learning Hierarchical Representations

Maximillian Nickel, Douwe Kiela

One-Sided Unsupervised Domain Mapping

Sagie Benaim, Lior Wolf

Regret Analysis for Continuous Dueling Bandit

Wataru Kumagai

Decoupling "when to update" from "how to update"

Eran Malach, Shai Shalev-shwartz

Continuous DR-submodular Maximization: Structure and Algorithms

An Bian, Kfir Levy, Andreas Krause, Joachim M Buhmann

Implicit Regularization in Matrix Factorization

Suriya Gunasekar, Blake E Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro

A simple model of recognition and recall memory

Nisheeth Srivastava, Edward Vul

Variable Importance Using Decision Trees

Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S Talwalkar

Renyi Differential Privacy Mechanisms for Posterior Sampling

Joseph Geumlek, Shuang Song, Kamalika Chaudhuri

Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces

Songbai Yan, Chicheng Zhang

Unbiased estimates for linear regression via volume sampling

Michal Derezinski, Manfred K. Warmuth

Off-policy evaluation for slate recommendation

Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford

Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts

Raymond Yeh, Jinjun Xiong, Wen-Mei Hwu, Minh Do, Alexander Schwing

Deep Hyperspherical Learning

Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai

Learning Unknown Markov Decision Processes: A Thompson Sampling Approach

Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar, Rahul Jain

A Meta-Learning Perspective on Cold-Start Recommendations for Items

Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle

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

On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models

Adarsh Prasad, Alexandru Niculescu-mizil, Pradeep K Ravikumar

Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks

Surbhi Goel, Adam Klivans

Cost efficient gradient boosting

Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler

Action Centered Contextual Bandits

Kristjan Greenewald, Ambuj Tewari, Susan Murphy, Predag Klasnja

OnACID: Online Analysis of Calcium Imaging Data in Real Time

Andrea Giovannucci, Johannes Friedrich, Matt Kaufman, Anne Churchland, Dmitri Chklovskii

Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems

Alyson K. Fletcher, Sundeep Rangan, Philip Schniter, Mojtaba Sahraee Ardakan, Mojtaba Sahraee-Ardakan

Concentration of Multilinear Functions of the Ising Model with Applications to Network Data

Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath

SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks

Kyuhong Shim, Minjae Lee, Iksoo Choi, Yoonho Boo, Wonyong Sung

Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data

Stéphanie ALLASSONNIERE, Juliette Chevallier, Stephane Oudard

Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma

Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang, Han Liu

Model evidence from nonequilibrium simulations

Michael Habeck

Adversarial Symmetric Variational Autoencoder

Yuchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan

Learning to See Physics via Visual De-animation

Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, Josh Tenenbaum

Deconvolutional Paragraph Representation Learning

Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao

Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space

Liwei Wang, Alexander Schwing, Svetlana Lazebnik

Selective Classification for Deep Neural Networks

Yonatan Geifman, Ran El-Yaniv

Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery

Jie Shen, Ping Li

Practical Data-Dependent Metric Compression with Provable Guarantees

Piotr Indyk, Ilya Razenshteyn, Tal Wagner

Ensemble Sampling

Xiuyuan Lu, Benjamin Van Roy

Universal Style Transfer via Feature Transforms

Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu

Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration

Jason Altschuler, Jonathan Niles-Weed, Philippe Rigollet

Learning to Pivot with Adversarial Networks

Gilles Louppe, Michael Kagan, Kyle Cranmer

SGD Learns the Conjugate Kernel Class of the Network

Amit Daniely

Linear Time Computation of Moments in Sum-Product Networks

Han Zhao, Geoffrey J. Gordon

Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net

Anirudh Goyal ALIAS PARTH GOYAL, Nan Rosemary Ke, Surya Ganguli, Yoshua Bengio

Contrastive Learning for Image Captioning

Bo Dai, Dahua Lin

Zap Q-Learning

Adithya M Devraj, Sean Meyn

Deep Lattice Networks and Partial Monotonic Functions

Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta

Cortical microcircuits as gated-recurrent neural networks

Rui Costa, Ioannis Alexandros Assael, Brendan Shillingford, Nando de Freitas, TIm Vogels

The Numerics of GANs

Lars Mescheder, Sebastian Nowozin, Andreas Geiger

Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data

Joel A Tropp, Alp Yurtsever, Madeleine Udell, Volkan Cevher

Hindsight Experience Replay

Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong

An Error Detection and Correction Framework for Connectomics

Jonathan Zung, Ignacio Tartavull, Kisuk Lee, H. Sebastian Seung

Local Aggregative Games

Vikas Garg, Tommi Jaakkola

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton

Approximation and Convergence Properties of Generative Adversarial Learning

Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri

VAE Learning via Stein Variational Gradient Descent

Yuchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han

Fader Networks:Manipulating Images by Sliding Attributes

Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic DENOYER

Task-based End-to-end Model Learning in Stochastic Optimization

Priya Donti, Brandon Amos, J. Zico Kolter

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, C. Lawrence Zitnick