NeurIPS 2018

1,009 papers

Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks

Qilong Wang, Zilin Gao, Jiangtao Xie, Wangmeng Zuo, Peihua Li

Learning a latent manifold of odor representations from neural responses in piriform cortex

Anqi Wu, Stan Pashkovski, Sandeep R Datta, Jonathan W Pillow

Learning towards Minimum Hyperspherical Energy

Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu

Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs

Han Shao, Xiaotian Yu, Irwin King, Michael R Lyu

A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication

Peng Jiang, Gagan Agrawal

Delta-encoder: an effective sample synthesis method for few-shot object recognition

Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder

Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis

Haoye Dong, Xiaodan Liang, Ke Gong, Hanjiang Lai, Jia Zhu

SNIPER: Efficient Multi-Scale Training

Bharat Singh, Mahyar Najibi, Larry S. Davis

Heterogeneous Multi-output Gaussian Process Prediction

Pablo Moreno-Muñoz, Antonio Artés, Mauricio Alvarez

Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters

Pavel Dvurechenskii, Darina Dvinskikh, Alexander Gasnikov, Cesar Uribe, Angelia Nedich

Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data

Xenia Miscouridou, Francois Caron, Yee Whye Teh

Recurrently Controlled Recurrent Networks

Yi Tay, Anh Tuan Luu, Siu Cheung Hui

Dual Policy Iteration

Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Bagnell

Generalized Zero-Shot Learning with Deep Calibration Network

Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan, Michael I Jordan

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator

Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang

Wasserstein Distributionally Robust Kalman Filtering

Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Huhn, Peyman Mohajerin Esfahani

Scalable Hyperparameter Transfer Learning

Valerio Perrone, Rodolphe Jenatton, Matthias W Seeger, Cédric Archambeau

Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres

Oisín Moran, Piergiorgio Caramazza, Daniele Faccio, Roderick Murray-Smith

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

Tengfei Ma, Jie Chen, Cao Xiao

CatBoost: unbiased boosting with categorical features

Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin

Provably Correct Automatic Sub-Differentiation for Qualified Programs

Sham M. Kakade, Jason Lee

Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization

Jie Cao, Yibo Hu, Hongwen Zhang, Ran He, Zhenan Sun

Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients?

Boris Hanin

Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN

Shupeng Su, Chao Zhang, Kai Han, Yonghong Tian

Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited

Di Wang, Marco Gaboardi, Jinhui Xu

The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization

Constantinos Daskalakis, Ioannis Panageas

On Markov Chain Gradient Descent

Tao Sun, Yuejiao Sun, Wotao Yin

Stochastic Nested Variance Reduction for Nonconvex Optimization

Dongruo Zhou, Pan Xu, Quanquan Gu

Bayesian Inference of Temporal Task Specifications from Demonstrations

Ankit Shah, Pritish Kamath, Julie A Shah, Shen Li

Semi-Supervised Learning with Declaratively Specified Entropy Constraints

Haitian Sun, William W. Cohen, Lidong Bing

Data-Driven Clustering via Parameterized Lloyd's Families

Maria-Florina F Balcan, Travis Dick, Colin White

Stein Variational Gradient Descent as Moment Matching

Qiang Liu, Dilin Wang

Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms

Kishan Wimalawarne, Hiroshi Mamitsuka

Bayesian Structure Learning by Recursive Bootstrap

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

Learning and Inference in Hilbert Space with Quantum Graphical Models

Siddarth Srinivasan, Carlton Downey, Byron Boots

Multi-Class Learning: From Theory to Algorithm

Jian Li, Yong Liu, Rong Yin, Hua Zhang, Lizhong Ding

Multimodal Generative Models for Scalable Weakly-Supervised Learning

Mike Wu, Noah Goodman

Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes

Loucas Pillaud-Vivien, Alessandro Rudi, Francis Bach

Learning in Games with Lossy Feedback

Zhengyuan Zhou, Panayotis Mertikopoulos, Susan Athey, Nicholas Bambos, Peter W. Glynn

Adaptive Online Learning in Dynamic Environments

Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou

Hessian-based Analysis of Large Batch Training and Robustness to Adversaries

Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney

Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks

Quan Zhang, Mingyuan Zhou

Random Feature Stein Discrepancies

Jonathan Huggins, Lester Mackey

A Model for Learned Bloom Filters and Optimizing by Sandwiching

Michael Mitzenmacher

Learning to Specialize with Knowledge Distillation for Visual Question Answering

Jonghwan Mun, Kimin Lee, Jinwoo Shin, Bohyung Han

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf

On Binary Classification in Extreme Regions

Hamid JALALZAI, Stéphan Clémençon, Anne Sabourin

Implicit Bias of Gradient Descent on Linear Convolutional Networks

Suriya Gunasekar, Jason Lee, Daniel Soudry, Nati Srebro

Neural Arithmetic Logic Units

Andrew Trask, Felix Hill, Scott E Reed, Jack Rae, Chris Dyer

Total stochastic gradient algorithms and applications in reinforcement learning

Paavo Parmas

Deep State Space Models for Unconditional Word Generation

Florian Schmidt, Thomas Hofmann

Maximum-Entropy Fine Grained Classification

Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik

Middle-Out Decoding

Shikib Mehri, Leonid Sigal

Provable Variational Inference for Constrained Log-Submodular Models

Josip Djolonga, Stefanie Jegelka, Andreas Krause

A Bayesian Nonparametric View on Count-Min Sketch

Diana Cai, Michael Mitzenmacher, Ryan P. Adams

Binary Rating Estimation with Graph Side Information

Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, Changho Suh

Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator

Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu

Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation

Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao

Human-in-the-Loop Interpretability Prior

Isaac Lage, Andrew Ross, Samuel J Gershman, Been Kim, Finale Doshi-velez

Efficient online algorithms for fast-rate regret bounds under sparsity

Pierre Gaillard, Olivier Wintenberger

Computing Higher Order Derivatives of Matrix and Tensor Expressions

Soeren Laue, Matthias Mitterreiter, Joachim Giesen

Enhancing the Accuracy and Fairness of Human Decision Making

Isabel Valera, Adish Singla, Manuel Gomez Rodriguez

Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer

David Madras, Toni Pitassi, Richard Zemel

A Dual Framework for Low-rank Tensor Completion

Madhav Nimishakavi, Pratik Kumar Jawanpuria, Bamdev Mishra

MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization

Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep K Ravikumar

IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis

Huaibo Huang, zhihang li, Ran He, Zhenan Sun, Tieniu Tan

Distributed Weight Consolidation: A Brain Segmentation Case Study

Patrick McClure, Charles Y Zheng, Jakub Kaczmarzyk, John Rogers-Lee, Satra Ghosh

Generalisation in humans and deep neural networks

Robert Geirhos, Carlos R. M. Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge

Unsupervised Learning of Artistic Styles with Archetypal Style Analysis

Daan Wynen, Cordelia Schmid, Julien Mairal

Contextual Stochastic Block Models

Yash Deshpande, Subhabrata Sen, Andrea Montanari, Elchanan Mossel

The streaming rollout of deep networks - towards fully model-parallel execution

Volker Fischer, Jan Koehler, Thomas Pfeil

Interactive Structure Learning with Structural Query-by-Committee

Christopher Tosh, Sanjoy Dasgupta

Learning Plannable Representations with Causal InfoGAN

Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, Pieter Abbeel

Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog

Sang-Woo Lee, Yu-Jung Heo, Byoung-tak Zhang

Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity

Conghui Tan, Tong Zhang, Shiqian Ma, Ji Liu

Differentially Private Bayesian Inference for Exponential Families

Garrett Bernstein, Daniel R. Sheldon

On gradient regularizers for MMD GANs

Michael Arbel, Danica J. Sutherland, Mikołaj Bińkowski, Arthur Gretton

Adversarial Text Generation via Feature-Mover's Distance

Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan

Escaping Saddle Points in Constrained Optimization

Aryan Mokhtari, Asuman Ozdaglar, Ali Jadbabaie

Probabilistic Neural Programmed Networks for Scene Generation

Zhiwei Deng, Jiacheng Chen, YIFANG FU, Greg Mori

Query K-means Clustering and the Double Dixie Cup Problem

I Chien, Chao Pan, Olgica Milenkovic

One-Shot Unsupervised Cross Domain Translation

Sagie Benaim, Lior Wolf

A General Method for Amortizing Variational Filtering

Joseph Marino, Milan Cvitkovic, Yisong Yue

Leveraging the Exact Likelihood of Deep Latent Variable Models

Pierre-Alexandre Mattei, Jes Frellsen

COLA: Decentralized Linear Learning

Lie He, An Bian, Martin Jaggi

Generalization Bounds for Uniformly Stable Algorithms

Vitaly Feldman, Jan Vondrak

Data center cooling using model-predictive control

Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy

Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification

Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai

Learning sparse neural networks via sensitivity-driven regularization

Enzo Tartaglione, Skjalg Lepsøy, Attilio Fiandrotti, Gianluca Francini

Critical initialisation for deep signal propagation in noisy rectifier neural networks

Arnu Pretorius, Elan van Biljon, Steve Kroon, Herman Kamper

Unsupervised Adversarial Invariance

Ayush Jaiswal, Rex Yue Wu, Wael Abd-Almageed, Prem Natarajan

Objective and efficient inference for couplings in neuronal networks

Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima

How Many Samples are Needed to Estimate a Convolutional Neural Network?

Simon S Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov

ResNet with one-neuron hidden layers is a Universal Approximator

Hongzhou Lin, Stefanie Jegelka

Learning SMaLL Predictors

Vikas Garg, Ofer Dekel, Lin Xiao

Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds

Kry Lui, Gavin Weiguang Ding, Ruitong Huang, Robert McCann

A Spectral View of Adversarially Robust Features

Shivam Garg, Vatsal Sharan, Brian Zhang, Gregory Valiant

Streamlining Variational Inference for Constraint Satisfaction Problems

Aditya Grover, Tudor Achim, Stefano Ermon

Identification and Estimation of Causal Effects from Dependent Data

Eli Sherman, Ilya Shpitser

Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net

Tom Michoel