NeurIPS 2016

569 papers

Estimating the class prior and posterior from noisy positives and unlabeled data

Shantanu Jain, Martha White, Predrag Radivojac

Stochastic Structured Prediction under Bandit Feedback

Artem Sokolov, Julia Kreutzer, Stefan Riezler, Christopher Lo

Global Analysis of Expectation Maximization for Mixtures of Two Gaussians

Ji Xu, Daniel J. Hsu, Arian Maleki

A posteriori error bounds for joint matrix decomposition problems

Nicolo Colombo, Nikos Vlassis

Stochastic Gradient Geodesic MCMC Methods

Chang Liu, Jun Zhu, Yang Song

Rényi Divergence Variational Inference

Yingzhen Li, Richard E Turner

Linear dynamical neural population models through nonlinear embeddings

Yuanjun Gao, Evan W Archer, Liam Paninski, John P. Cunningham

Boosting with Abstention

Corinna Cortes, Giulia DeSalvo, Mehryar Mohri

Communication-Optimal Distributed Clustering

Jiecao Chen, He Sun, David Woodruff, Qin Zhang

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations

Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro

A primal-dual method for conic constrained distributed optimization problems

Necdet Serhat Aybat, Erfan Yazdandoost Hamedani

Wasserstein Training of Restricted Boltzmann Machines

Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi

Understanding Probabilistic Sparse Gaussian Process Approximations

Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen

Latent Attention For If-Then Program Synthesis

Chang Liu, Xinyun Chen, Eui Chul Shin, Mingcheng Chen, Dawn Song

Bayesian latent structure discovery from multi-neuron recordings

Scott Linderman, Ryan P. Adams, Jonathan W Pillow

CRF-CNN: Modeling Structured Information in Human Pose Estimation

Xiao Chu, Wanli Ouyang, hongsheng Li, Xiaogang Wang

Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments

Ransalu Senanayake, Lionel Ott, Simon O'Callaghan, Fabio T Ramos

Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes

Chris Junchi Li, Zhaoran Wang, Han Liu

Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition

Jinzhuo Wang, Wenmin Wang, xiongtao Chen, Ronggang Wang, Wen Gao

Learning the Number of Neurons in Deep Networks

Mathieu Salzmann, Jose M. Alvarez, Jose M Alvarez

The Multiscale Laplacian Graph Kernel

Risi Kondor, Horace Pan

A Unified Approach for Learning the Parameters of Sum-Product Networks

Han Zhao, Pascal Poupart, Geoffrey J. Gordon

Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation

Emmanuel Abbe, Colin Sandon

Probabilistic Inference with Generating Functions for Poisson Latent Variable Models

Kevin Winner, Daniel R. Sheldon

Learning Sparse Gaussian Graphical Models with Overlapping Blocks

Mohammad Javad Hosseini, Su-in Lee

Improved Deep Metric Learning with Multi-class N-pair Loss Objective

Kihyuk Sohn

Ladder Variational Autoencoders

Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther

Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation

George Papamakarios, Iain Murray

Equality of Opportunity in Supervised Learning

Moritz Hardt, Eric Price, ecprice, Nati Srebro

Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain

Timothy Rubin, Oluwasanmi O Koyejo, Michael N. Jones, Tal Yarkoni

On the Recursive Teaching Dimension of VC Classes

Xi Chen, Yu Cheng, Bo Tang

Backprop KF: Learning Discriminative Deterministic State Estimators

Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel

Learning Influence Functions from Incomplete Observations

Xinran He, Ke Xu, David Kempe, Yan Liu

An ensemble diversity approach to supervised binary hashing

Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei

Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering

Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah

Flexible Models for Microclustering with Application to Entity Resolution

Brenda Betancourt, Giacomo Zanella, Jeffrey W Miller, Hanna Wallach, Abbas Zaidi

Data Programming: Creating Large Training Sets, Quickly

Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, Christopher Re

Computing and maximizing influence in linear threshold and triggering models

Justin T Khim, Varun Jog, Po-ling Loh

CliqueCNN: Deep Unsupervised Exemplar Learning

Miguel A Bautista, Artsiom Sanakoyeu, Ekaterina Tikhoncheva, Bjorn Ommer

SURGE: Surface Regularized Geometry Estimation from a Single Image

Peng Wang, Xiaohui Shen, Bryan Russell, Scott Cohen, Brian Price

Verification Based Solution for Structured MAB Problems

Zohar S Karnin

A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control

Ivan Herreros, Xerxes Arsiwalla, Paul Verschure

Tight Complexity Bounds for Optimizing Composite Objectives

Blake E Woodworth, Nati Srebro

Dynamic matrix recovery from incomplete observations under an exact low-rank constraint

Liangbei Xu, Mark Davenport

Fast learning rates with heavy-tailed losses

Vu C Dinh, Lam S Ho, Binh Nguyen, Duy Nguyen

Large Margin Discriminant Dimensionality Reduction in Prediction Space

Mohammad Saberian, Jose Costa Pereira, Can Xu, Jian Yang, Nuno Nvasconcelos

Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula

jean barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur

Learning Parametric Sparse Models for Image Super-Resolution

Yongbo Li, Weisheng Dong, Xuemei Xie, GUANGMING Shi, Xin Li

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B Oliva, Jeff Schneider, Barnabás Póczos

Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach

Remi Lam, Karen Willcox, David H. Wolpert

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune

Stochastic Three-Composite Convex Minimization

Alp Yurtsever, Bang Cong Vu, Volkan Cevher

Only H is left: Near-tight Episodic PAC RL

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

Daniel Neil, Michael Pfeiffer, Shih-Chii Liu

Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow

Gang Wang, Georgios Giannakis

Dual Learning for Machine Translation

Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu

Tractable Operations for Arithmetic Circuits of Probabilistic Models

Yujia Shen, Arthur Choi, Adnan Darwiche

Sparse Support Recovery with Non-smooth Loss Functions

Kévin Degraux, Gabriel Peyré, Jalal Fadili, Laurent Jacques

Supervised learning through the lens of compression

Ofir David, Shay Moran, Amir Yehudayoff

More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning

Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu

Privacy Odometers and Filters: Pay-as-you-Go Composition

Ryan M Rogers, Aaron Roth, Jonathan Ullman, Salil Vadhan

A Powerful Generative Model Using Random Weights for the Deep Image Representation

Kun He, Yan Wang, John Hopcroft

Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models

Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel

R-FCN: Object Detection via Region-based Fully Convolutional Networks

Jifeng Dai, Yi Li, Kaiming He, Jian Sun

Examples are not enough, learn to criticize! Criticism for Interpretability

Been Kim, Rajiv Khanna, Oluwasanmi O Koyejo

Sub-sampled Newton Methods with Non-uniform Sampling

Peng Xu, Jiyan Yang, Fred Roosta, Christopher Re, Michael W. Mahoney

Learning Multiagent Communication with Backpropagation

Sainbayar Sukhbaatar, arthur szlam, Rob Fergus

Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities

Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári

Local Similarity-Aware Deep Feature Embedding

Chen Huang, Chen Change Loy, Xiaoou Tang

GAP Safe Screening Rules for Sparse-Group Lasso

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

Online Convex Optimization with Unconstrained Domains and Losses

Ashok Cutkosky, Kwabena A. Boahen

Convex Two-Layer Modeling with Latent Structure

Vignesh Ganapathiraman, Xinhua Zhang, Yaoliang Yu, Junfeng Wen

Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions

Yichen Wang, Nan Du, Rakshit Trivedi, Le Song

Orthogonal Random Features

Felix Xinnan X Yu, Ananda Theertha Suresh, Krzysztof M Choromanski, Daniel N Holtmann-Rice, Sanjiv Kumar

The Multiple Quantile Graphical Model

Alnur Ali, J. Zico Kolter, Ryan J Tibshirani

Structured Prediction Theory Based on Factor Graph Complexity

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang

Supervised Learning with Tensor Networks

Edwin Stoudenmire, David J Schwab

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari

Learning values across many orders of magnitude

Hado P van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver

Multiple-Play Bandits in the Position-Based Model

Paul Lagrée, Claire Vernade, Olivier Cappe

Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model

Zhen Xu, Wen Dong, Sargur N Srihari

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Ilya O Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf

Coupled Generative Adversarial Networks

Ming-Yu Liu, Oncel Tuzel

A Simple Practical Accelerated Method for Finite Sums

Aaron Defazio

Search Improves Label for Active Learning

Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang

Finite Sample Prediction and Recovery Bounds for Ordinal Embedding

Lalit Jain, Kevin G. Jamieson, Rob Nowak

SDP Relaxation with Randomized Rounding for Energy Disaggregation

Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu

Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

Jianxu Chen, Lin Yang, Yizhe Zhang, Mark Alber, Danny Z Chen

Hierarchical Clustering via Spreading Metrics

Aurko Roy, Sebastian Pokutta

Consistent Kernel Mean Estimation for Functions of Random Variables

Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O Tolstikhin, Bernhard Schölkopf

Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters

Zeyuan Allen-Zhu, Yang Yuan, Karthik Sridharan

Towards Conceptual Compression

Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra

An Architecture for Deep, Hierarchical Generative Models

Philip Bachman

Identification and Overidentification of Linear Structural Equation Models

Bryant Chen

Probing the Compositionality of Intuitive Functions

Eric Schulz, Josh Tenenbaum, David K. Duvenaud, Maarten Speekenbrink, Samuel J Gershman

NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization

Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang

Multi-armed Bandits: Competing with Optimal Sequences

Zohar S Karnin, Oren Anava

An algorithm for L1 nearest neighbor search via monotonic embedding

Xinan Wang, Sanjoy Dasgupta

Assortment Optimization Under the Mallows model

Antoine Desir, Vineet Goyal, Srikanth Jagabathula, Danny Segev

A Probabilistic Programming Approach To Probabilistic Data Analysis

Feras Saad, Vikash K. Mansinghka, Vikash K Mansinghka