NeurIPS 2014

411 papers

Spectral Learning of Mixture of Hidden Markov Models

Cem Subakan, Johannes Traa, Paris Smaragdis

Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm

Jun Zhu, Junhua Mao, Alan L. Yuille

DFacTo: Distributed Factorization of Tensors

Joon Hee Choi, S. Vishwanathan

Self-Adaptable Templates for Feature Coding

Xavier Boix, Gemma Roig, Salomon Diether, Luc V Gool, Luc V. Gool

A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs

Miles Lopes

A Representation Theory for Ranking Functions

Harsh H Pareek, Pradeep K Ravikumar

A framework for studying synaptic plasticity with neural spike train data

Scott Linderman, Christopher H Stock, Ryan P. Adams

Scalable Inference for Neuronal Connectivity from Calcium Imaging

Alyson K. Fletcher, Sundeep Rangan

Robust Bayesian Max-Margin Clustering

Changyou Chen, Jun Zhu, Xinhua Zhang

Neural Word Embedding as Implicit Matrix Factorization

Omer Levy, Yoav Goldberg

Multi-Class Deep Boosting

Vitaly Kuznetsov, Mehryar Mohri, Umar Syed

A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input

Mateusz Malinowski, Mario Fritz

Bayesian Sampling Using Stochastic Gradient Thermostats

Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D Skeel

Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space

Robert A Vandermeulen, Clayton Scott

Multiscale Fields of Patterns

Pedro Felzenszwalb, John G Oberlin

Learning Mixed Multinomial Logit Model from Ordinal Data

Sewoong Oh, Devavrat Shah

Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm

Deanna Needell, Rachel Ward, Nati Srebro

Conditional Random Field Autoencoders for Unsupervised Structured Prediction

Waleed Ammar, Chris Dyer, Noah A. Smith

Smoothed Gradients for Stochastic Variational Inference

Stephan Mandt, David Blei

Probabilistic Differential Dynamic Programming

Yunpeng Pan, Evangelos Theodorou

A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment

Sahar Akram, Jonathan Z Simon, Shihab A Shamma, Behtash Babadi

Do Deep Nets Really Need to be Deep?

Jimmy Ba, Rich Caruana

Model-based Reinforcement Learning and the Eluder Dimension

Ian Osband, Benjamin Van Roy

Iterative Neural Autoregressive Distribution Estimator NADE-k

Tapani Raiko, Yao Li, Kyunghyun Cho, Yoshua Bengio

Scale Adaptive Blind Deblurring

Haichao Zhang, Jianchao Yang

Subspace Embeddings for the Polynomial Kernel

Haim Avron, Huy Nguyen, David Woodruff

Modeling Deep Temporal Dependencies with Recurrent Grammar Cells""

Vincent Michalski, Roland Memisevic, Kishore Konda

Fast Training of Pose Detectors in the Fourier Domain

João F. Henriques, Pedro Martins, Rui F Caseiro, Jorge Batista

Convolutional Neural Network Architectures for Matching Natural Language Sentences

Baotian Hu, Zhengdong Lu, Hang Li, Qingcai Chen

Advances in Learning Bayesian Networks of Bounded Treewidth

Siqi Nie, Denis D. Maua, Cassio P de Campos, Qiang Ji

Nonparametric Bayesian inference on multivariate exponential families

William R Vega-Brown, Marek Doniec, Nicholas G Roy

Ranking via Robust Binary Classification

Hyokun Yun, Parameswaran Raman, S. Vishwanathan

Fast and Robust Least Squares Estimation in Corrupted Linear Models

Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M Buhmann

Global Belief Recursive Neural Networks

Romain Paulus, Richard Socher, Christopher D. Manning

Accelerated Mini-batch Randomized Block Coordinate Descent Method

Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu

Mind the Nuisance: Gaussian Process Classification using Privileged Noise

Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto

Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection

Sang Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam

Optimal rates for k-NN density and mode estimation

Sanjoy Dasgupta, Samory Kpotufe

Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning

Robert Lindsey, Mohammad Khajah, Michael Mozer

Structure Regularization for Structured Prediction

Xu Sun

Robust Tensor Decomposition with Gross Corruption

Quanquan Gu, Huan Gui, Jiawei Han

Inferring synaptic conductances from spike trains with a biophysically inspired point process model

Kenneth W Latimer, E. J. Chichilnisky, Fred Rieke, Jonathan W Pillow

Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials

Shenlong Wang, Alex Schwing, Raquel Urtasun

Deterministic Symmetric Positive Semidefinite Matrix Completion

William E Bishop, Byron M. Yu

Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models

Yichuan Zhang, Charles Sutton

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

Tom Gunter, Michael A Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts

Altitude Training: Strong Bounds for Single-Layer Dropout

Stefan Wager, William Fithian, Sida Wang, Percy Liang

Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling

Ricardo Henao, Xin Yuan, Lawrence Carin

Incremental Local Gaussian Regression

Franziska Meier, Philipp Hennig, Stefan Schaal

Clustering from Labels and Time-Varying Graphs

Shiau Hong Lim, Yudong Chen, Huan Xu

Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures

Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, Ashkan Jafarpour

Median Selection Subset Aggregation for Parallel Inference

Xiangyu Wang, Peichao Peng, David B. Dunson, David B Dunson

Object Localization based on Structural SVM using Privileged Information

Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han

Weakly-supervised Discovery of Visual Pattern Configurations

Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell

On the Computational Efficiency of Training Neural Networks

Roi Livni, Shai Shalev-shwartz, Ohad Shamir

Graph Clustering With Missing Data: Convex Algorithms and Analysis

Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi

A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights

Weijie Su, Stephen Boyd, Emmanuel Candes

Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit

Karin C Knudson, Jacob Yates, Alexander Huk, Jonathan W Pillow

Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators

Kai Zhong, Ian En-Hsu Yen, Inderjit S Dhillon, Pradeep K Ravikumar

Consistent Binary Classification with Generalized Performance Metrics

Oluwasanmi O Koyejo, Nagarajan Natarajan, Pradeep K Ravikumar, Inderjit S Dhillon

Recursive Context Propagation Network for Semantic Scene Labeling

Abhishek Sharma, Oncel Tuzel, Ming-Yu Liu

Optimal decision-making with time-varying evidence reliability

Jan Drugowitsch, Ruben Moreno-Bote, Alexandre Pouget

Online and Stochastic Gradient Methods for Non-decomposable Loss Functions

Purushottam Kar, Harikrishna Narasimhan, Prateek Jain

Multi-scale Graphical Models for Spatio-Temporal Processes

Firdaus Janoos, Huseyin Denli, Niranjan Subrahmanya

Learning Distributed Representations for Structured Output Prediction

Vivek Srikumar, Christopher D. Manning

A Boosting Framework on Grounds of Online Learning

Tofigh Naghibi Mohamadpoor, Beat Pfister

A* Sampling

Chris J Maddison, Daniel Tarlow, Tom Minka

SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

Aaron Defazio, Francis Bach, Simon Lacoste-Julien

Communication Efficient Distributed Machine Learning with the Parameter Server

Mu Li, David G Andersen, Alexander J Smola, Kai Yu

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

David Eigen, Christian Puhrsch, Rob Fergus

Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards

Omar Besbes, Yonatan Gur, Assaf Zeevi

Distributed Bayesian Posterior Sampling via Moment Sharing

Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang

Learning to Optimize via Information-Directed Sampling

Daniel Russo, Benjamin Van Roy

Parallel Feature Selection Inspired by Group Testing

Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q Ngo, Xuanlong Nguyen

Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets

Adarsh Prasad, Stefanie Jegelka, Dhruv Batra

Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities

Tianbao Yang, Rong Jin

Optimal Neural Codes for Control and Estimation

Alex K. Susemihl, Ron Meir, Manfred Opper

Extended and Unscented Gaussian Processes

Daniel M Steinberg, Edwin V. Bonilla

Randomized Experimental Design for Causal Graph Discovery

Huining Hu, Zhentao Li, Adrian R Vetta

Exploiting easy data in online optimization

Amir Sani, Gergely Neu, Alessandro Lazaric

Sensory Integration and Density Estimation

Joseph G Makin, Philip N. Sabes

Computing Nash Equilibria in Generalized Interdependent Security Games

Hau Chan, Luis E. Ortiz

Restricted Boltzmann machines modeling human choice

Takayuki Osogami, Makoto Otsuka

Expectation-Maximization for Learning Determinantal Point Processes

Jennifer A Gillenwater, Alex Kulesza, Emily B. Fox, Ben Taskar

Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation

Jonathan J Tompson, Arjun Jain, Yann LeCun, Christoph Bregler

Bayesian Inference for Structured Spike and Slab Priors

Michael R Andersen, Ole Winther, Lars K. Hansen

Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang

Analog Memories in a Balanced Rate-Based Network of E-I Neurons

Dylan Festa, Guillaume Hennequin, Máté Lengyel

Orbit Regularization

Renato Negrinho, Andre Martins

Learning with Pseudo-Ensembles

Philip Bachman, Ouais Alsharif, Doina Precup

General Stochastic Networks for Classification

Matthias Zöhrer, Franz Pernkopf

The Large Margin Mechanism for Differentially Private Maximization

Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song

Algorithm selection by rational metareasoning as a model of human strategy selection

Falk Lieder, Dillon Plunkett, Jessica B Hamrick, Stuart Russell, Nicholas Hay

Information-based learning by agents in unbounded state spaces

Shariq A Mobin, James A Arnemann, Fritz Sommer

Self-Paced Learning with Diversity

Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan

Variational Gaussian Process State-Space Models

Roger Frigola, Yutian Chen, Carl Edward Rasmussen

Spectral k-Support Norm Regularization

Andrew M McDonald, Massimiliano Pontil, Dimitris Stamos

Asynchronous Anytime Sequential Monte Carlo

Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh

Multi-Resolution Cascades for Multiclass Object Detection

Mohammad Saberian, Nuno Vasconcelos

Tree-structured Gaussian Process Approximations

Thang D Bui, Richard E Turner