NIPS Salon des Refusés

Exhibition number 1 deriving from decisions at NIPS 2016

Contents:

Papers in the Exhibition

Pre-Conference Listing

Papers submitted to the Salon before the NIPS conference.

Linear Thompson Sampling Revisited

Marc Abeille (Inria-Lille); Alessandro Lazaric (Inria-Lille);

Unbiased Sparse Subspace Clustering By Selective Pursuit

Hanno Ackermann (Hanover University); Michael Yang (Twente University); Bodo Rosenhahn (Hanover University);

Learning Bayesian Networks with Incomplete Data by Augmentation

Tameem Adel; Cassio P. de Campos;

Improved Multi-Class Cost-Sensitive Boosting via Estimation of the Minimum-Risk Class

Ron Appel (Caltech); Xavier Burgos-Artizzu (THX); Pietro Perona (Caltech);

Towards Optimality Conditions for Non-Linear Networks

Devansh Arpit (SUNY Buffalo); Hung Q. Ngo (LogicBlox); Yingbo Zhou (SUNY Buffalo); Nils Napp (SUNY Buffalo); Venu Govindaraju (SUNY Buffalo);

The Option-Critic Architecture

Pierre-Luc Bacon (McGill University); Jean Harb (McGill University); Doina Precup (McGill University);

Defining the Neural Code

Thomas Bangert (Queen Mary University of London); Ebroul Izquierdo (Queen Mary University of London);

Kernel regression, minimax rates and effective dimensionality: beyond the regular case

Gilles Blanchard (Potsdam University); Nicole Mücke (Potsdam University);

Convergence Rate Analysis of a Stochastic Trust Region Method for Nonconvex Optimization

Jose Blanchet (Columbia University); Coralia Cartis (University of Oxford); Matt Menickelly (Lehigh University); Katya Scheinberg (Lehigh University);

Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention

Théodore Bluche (A2iA); Jérôme Louradour (A2iA); Ronaldo Messina (A2iA);

Crowdsourcing: Low Complexity, Minimax Optimal Algorithms

Thomas Bonald (Telecom ParisTech); Richard Combes (Centrale-Supelec);

Dataflow matrix machines as programmable, dynamically expandable, self-referential generalized recurrent neural networks

Michael Bukatin (HERE North America LLC); Steve Matthews (University of Warwick); Andrey Radul (Project Fluid);

Stability revisited: new generalisation bounds for the Leave-one-Out

Alain Celisse (Université de Lille); Benjamin Guedj (Inria);

On the Optimal Sample Complexity for Best Arm Identification

Lijie Chen (Tsinghua University); Jian Li (Tsinghua University);

Predictive Coding for Dynamic Vision: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model

Minkyu Choi (KAIST); Jun Tani (KAIST);

Collaborative Filtering with Recurrent Neural Networks

Robin Devooght (ULB, IRIDIA); Hugues Bersini (ULB, IRIDIA);

Perceptual Reward Functions

Ashley Edwards (Georgia Institute of Technology); Charles Isbell (Georgia Institute of Technology); Atsuo Takanishi (Waseda University);

Stochastic Patching Process

Xuhui Fan (Data61, CSIRO, Australia); Bin Li (Data61, CSIRO, Australia); Yi Wang (Data61, CSIRO, Australia); Yang Wang (Data61, CSIRO, Australia); Fang Chen (Data61, CSIRO, Australia);

Cognitive Discriminative Mappings for Rapid Learning

Wen-Chieh Fang; Yi-ting Chiang;

Network of Bandits

Raphaël Féraud (Orange Labs);

Bayesian Opponent Exploitation in Imperfect-Information Games

Sam Ganzfried (Florida International University);

Optimal Number of Choices in Rating Contexts

Sam Ganzfried (Florida International University);

The Linearization of Belief Propagation on Pairwise Markov Random Fields

Wolfgang Gatterbauer (Carnegie Mellon University);

Causal inference for cloud computing

Philipp Geiger (MPI for Intelligent Systems); Lucian Carata (Univeristy of Cambridge); Bernhard Schölkopf (MPI for Intelligent Systems);

One Class Splitting Criteria for Random Forests with Application to Anomaly Detection

Nicolas Goix (Télécom Paristech); Romain Brault (Télécom Paristech); Nicolas Drougard (ISAE); Maël Chiapino (Télécom Paristech);

Faster Low-rank Approximation using Adaptive Gap-based Preconditioning

Alon Gonen (Hebrew University of Jeruslaem); Shai Shalev-Shwartz;

A Robust Adaptive Stochastic Gradient Method for Deep Learning

Caglar Gulcehre; Jose Sotelo; Marcin Moczulski; Yoshua Bengio;

The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs

Yong Guo (South China University of Technology); Mingkui Tan (South China University of Technology); Qingyao Wu (South China University of Technology); Jian Chen (South China University of Technology); Anton Van Den Hengel (The University of Adelaide); Qinfeng Shi (The University of Adelaide);

Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition

Furong Huang (Microsoft Research); Animashree Anandkumar (UC Irvine);

Fast Learning of Clusters and Topics via Sparse Posteriors

Michael C. Hughes (Brown University); Erik B. Sudderth (Brown University);

Training Spiking Deep Networks for Neuromorphic Hardware

Eric Hunsberger (University of Waterloo); Chris Eliasmith (University of Waterloo);

Character-Level Language Modeling with Hierarchical Recurrent Neural Networks

Kyuyeon Hwang (Seoul National University); Wonyong Sung (Seoul National University);

Learning Unitary Operators with Help From u(n)

Stephanie L. Hyland (ETH Zurich); Gunnar Rätsch (ETH Zurich);

Generating images with recurrent adversarial networks

Daniel Jiwoong Im; Chris Dongjoo Kim; Hui Jiang; Roland Memisevic;

How to scale distributed deep learning?

Peter H. Jin (UC Berkeley); Qiaochu Yuan (UC Berkeley); Forrest Iandola (UC Berkeley); Kurt Keutzer (UC Berkeley);

Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data

Maximilian Karl (Technische Universität München); Maximilian Soelch (Technische Universität München); Justin Bayer (Data Lab, Volkswagen Group); Patrick van der Smagt (Data Lab, Volkswagen Group);

Estimating Uncertainty Online Against an Adversary

Volodymyr Kuleshov; Stefano Ermon;

Asaga: Asynchronous Parallel SAGA

Rémi Leblond (Ecole Normale Supérieure / INRIA Sierra); Fabian Pedregosa (Ecole Normale Supérieure / INRIA Sierra); Simon Lacoste-Julien (Department of (CS & OR DIRO) Université de Montréal);

Generalized Min-Max Kernel and Generalized Consistent Weighted Sampling

Ping Li;

Learning to Optimize

Ke Li (UC Berkeley); Jitendra Malik (UC Berkeley);

Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

Tiger W. Lin (UCSD/Salk); Anup Das (UCSD); Giri P. Krishnan (UCSD); Maxim Bazhenov (UCSD); Terrence J. Sejnowski (UCSD/Salk);

Leveraging Union of Subspace Structure to Improve Constrained Clustering

John Lipor (University of Michigan, Ann Arbor); Laura Balzano (University of Michigan, Ann Arbor);

Multiple Kernel k-means with Incomplete Kernels

Xinwang Liu (NUDT); Miaomiao Li (NUDT); Lei Wang (NUDT); Yong Dou (NUDT); Jinping Yin (NUDT); En Zhu (NUDT);

On Minimal Accuracy Algorithm Selection in Computer Vision and Intelligent Systems

Martin Lukac (Nazarbayev University); Kamila Abdiyeva (Nazarbayev University); Michitaka Kameyama (Ishinomaki University);

Active Search for Sparse Signals with Region Sensing

Yifei Ma (Carnegie Mellon University); Roman Garnett (Washington University in St. Louis); Jeff Schneider (Carnegie Mellon University);

A performance-based approach to design the stimulus presentation paradigm for the P300-based BCI

Boyla Mainsah (Duke University); Galen Reeves (Duke University); Leslie Collins (Duke University); Chandra Throckmorton ;

Quantifying the probable approximation error of probabilistic inference programs

Marco F. Cusumano-Towner (MIT); Vikash K. Mansinghka (MIT);

Exploring and measuring non-linear correlations: Copulas, Lightspeed Transportation and Clustering

Gautier Marti (Hellebore Capital Ltd); Sébastien Andler (ENS de Lyon); Frank Nielsen (Ecole Polytechnique); Philippe Donnat (Hellebore Capital Ltd);

A Marginal-Based Technique for Distribution Estimation

Rajasekaran Masatran (IIT Madras);

A Modular Theory of Feature Learning

Daniel McNamara (Australian National University and Data61); Cheng Soon Ong (Australian National University and Data61); Robert C. Williamson (Australian National University and Data61;);

Learning from Binary Labels with Instance-Dependent Corruption

Aditya Krishna Menon (Data61); Brendan van Rooyen (QUT); Nagarajan Natarajan (MSR Bangalore);

OESO: Unfolding the Oesomeric Space of Reinforcement Learning Temporal models

Pierre Michaud (IPC);

Adversarial Training Methods for Semi-Supervised Text Classification

Takeru Miyato (Kyoto Univ., Google Brain); Andrew M. Dai (Google Brain); Ian Goodfellow (OpenAI);

Inductive quantum learning: Why you are doing it almost right

Alex Monràs (Universitat Autònoma de Barcelona); Gael Sentís (Universidad del País Vasco); Peter Wittek (ICFO-The Institute of Photonic Sciences);

Node-Adapt, Path-Adapt and Tree-Adapt: Model-Transfer Domain Adaptation for Random Forest

Azadeh S. Mozafari (Computer Engineering Department, Sharif university of Technology); David Vazquez (Computer Vision Center, UAB University); Mansour Jamzad (Computer Engineering Department, Sharif university of Technology); Antonio M. Lopez (Computer Vision Center, UAB University);

Neural Sampling by Irregular Gating Inhibition of Spiking Neurons and Attractor Networks

Lorenz K. Muller (Institute of Neuroinformatics, ETH Zurich and University of Zurich); Giacomo Indiveri (Institute of Neuroinformatics, ETH Zurich and University of Zurich);

Neural Semantic Encoders

Tsendsuren Munkhdalai (University of Massachusetts); Hong Yu (University of Massachusetts);

Word2Vec is a special case of Kernel Correspondence Analysis and Kernels for Natural Language Processing

Hirotaka Niitsuma; Minho Lee;

Practical optimal experiment design with probabilistic programs

Long Ouyang (Stanford); Michael Henry Tessler (Stanford); Daniel Ly (Stanford); Noah D. Goodman (Stanford);

Herding Generalizes Diverse M-Best Solutions

Ece Ozkan; Gemma Roig; Orcun Goksel; Xavier Boix;

DropNeuron: An Approach for Simplifying the Structure of Deep Neural Networks

Wei Pan; Hao Dong; Yike Guo;

Holistic SparseCNN: Forging the Trident of Accuracy, Speed, and Size

Jongsoo Park (Intel Corporation); Sheng R. Li (Intel Corporation); Wei Wen (University of Pittsburgh); Hai Li (University of Pittsburgh); Yiran Chen (University of Pittsburgh); Pradeep Dubey (Intel Corporation);

On Enumerating Stable Configurations of Cellular Automata with the MAJORITY update rule

Predrag T. Tosic;

A Latent-Variable Lattice Model

Rajasekaran Masatran (IIT Madras);

Differential response of the retinal neural code with respect to the sparseness of natural images

Cesar Ravello (CINV); Maria-Jose Escobar (Univ Tecnico Federico Santa María); Adrian Palacios (CINV); Laurent U. Perrinet (INT);

On numerical approximation schemes for expectation propagation

Alexis Roche (CHUV);

Action Classification via Concepts and Attributes

Amir Rosenfeld (Weizmann Institute of Science); Shimon Ullman (Weizmann Institute of Science);

Learning activation functions from data using cubic spline interpolation

Simone Scardapane (Sapienza University of Rome); Michele Scarpiniti (Sapienza University of Rome); Danilo Comminiello (Sapienza University of Rome); Aurelio Uncini (Sapienza University of Rome);

Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization

Ramprasaath R. Selvaraju (Virginia Tech); Abhishek Das (Virginia Tech); Ramakrishna Vedantam (Virginia Tech); Michael Cogswell (Virginia Tech); Devi Parikh (Georgia Tech); Dhruv Batra (Georgia Tech);

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Iulian Vlad Serban (University of Montreal); Alessandro Sordoni (University of Montreal); Ryan Lowe (McGill University); Laurent Charlin (McGill University); Joelle Pineau (McGill University); Aaron Courville (University of Montreal); Yoshua Bengio (University of Montreal);

Exploring Semantic Correspondence in Deep Convolutional Neural Networks

Zhiqiang Shen (Fudan University); Xiangyang Xue (Fudan University);

ProjE: Embedding Projection for Knowledge Graph Completion

Baoxu Shi (University of Notre Dame); Tim Weninger (University of Notre Dame);

Differentially Private Gaussian Processes

Michael Thomas Smith (University of Sheffield); Max Zwiessele (University of Sheffield); Neil D. Lawrence (University of Sheffield);

Higher Order Recurrent Neural Networks

Rohollah Soltani (York University); Hui Jiang (York University);

Scalable and Sustainable Deep Learning via Randomized Hashing

Ryan Spring (Rice University); Anshumali Shrivastava (Rice University);

Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation

Akash Srivastava (Informatics Forum, University of Edinburgh); James Zou (Microsoft Research and Stanford University); Charles Sutton (Informatics Forum, University of Edinburgh);

Convergence rate of stochastic k-means

Cheng Tang (George Washington University); Claire Monteleoni (George Washington University);

Recoverability of Joint Distribution from Missing Data

Jin Tian (Iowa State University);

Reducing the error of Monte Carlo Algorithms by Learning Control Variates

Brendan Tracey (MIT, Santa Fe Institute); David Wolpert (Santa Fe Institute, ASU);

Sifting Common Information from Many Variables

Greg Ver Steeg (USC); Shuyang Gao (USC); Kyle Reing (USC); Aram Galstyan (USC);

Generalizing the Convolution Operator to Extend CNNs to Irregular Domains

Jean-Charles Vialatte (Cityzen Data, Telecom Bretagne); Vincent Gripon (Telecom Bretagne); Grégoire Mercier (Telecom Bretagne);

Diverse Beam Search: Decoding Diverse Sequences from Neural Sequence Models

Ashwin K. Vijayakumar (Virginia Tech); Michael Cogswell (Virginia Tech); Ramprasaath R. Selvaraju (Virginia Tech); Qing Sun (Virginia Tech); Stefan Lee (Virginia Tech); David Crandall (Indiana University); Dhruv Batra (Virginia Tech);

Learning Sparse, Distributed Representations using the Hebbian Principle

Aseem Wadhwa (University of California Santa Barbara); Upamanyu Madhow (University of California Santa Barbara);

Reweighted Data for Robust Probabilistic Models

Yixin Wang (Columbia University); Alp Kucukelbir (Columbia University); David M. Blei (Columbia University);

Joint Dimensionality Reduction for Two Feature Vectors

Yanjun Li (UIUC); Yoram Bresler (UIUC);

Leveraging Video Descriptions to Learn Video Question Answering

Kuo-Hao Zeng (Stanford University); Tseng-Hung Chen (National Tsing Hua University); Ching-Yao Chuang (National Tsing Hua University); Yuan-Hong Liao (National Tsing Hua University); Juan Carlos Niebles (Stanford University); Min Sun (National Tsing Hua University);

Query-Efficient Imitation Learning for End-to-End Autonomous Driving

Jiakai Zhang (NYU); Kyunghyun Cho (NYU);

Infinite-Label Learning with Semantic Output Codes

Yang Zhang (University of Central Florida); Rupam Acharyya (University of Rochester); Ji Liu (University of Rochester); Boqing Gong (University of Central Florida);

Universum Prescription: Regularization using Unlabeled Data

Xiang Zhang (New York University); Yann LeCun (New York University);

Post-Conference Listing

Papers submitted to the Salon after the NIPS conference.

Adiabatic Persistent Contrastive Divergence Learning

Hyeryung Jang (KAIST); Hyungwon Choi (KAIST); Yung Yi (KAIST); Jinwoo Shin (KAIST);

Spreadsheet Probabilistic Programming

Mike Wu (Invrea); Yura Perov (Invrea); Frank Wood (Invrea); Hongseok Yang (Invrea);

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