NeurIPS 2014

411 papers

Non-convex Robust PCA

Praneeth Netrapalli, Niranjan U N, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain

Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion

Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng

Clamping Variables and Approximate Inference

Adrian Weller, Tony Jebara

Consistency of weighted majority votes

Daniel Berend, Aryeh Kontorovich

Bandit Convex Optimization: Towards Tight Bounds

Elad Hazan, Kfir Levy

On Prior Distributions and Approximate Inference for Structured Variables

Oluwasanmi O Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack

Convex Optimization Procedure for Clustering: Theoretical Revisit

Changbo Zhu, Huan Xu, Chenlei Leng, Shuicheng Yan

The Noisy Power Method: A Meta Algorithm with Applications

Moritz Hardt, Eric Price

Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time

Zhaoran Wang, Huanran Lu, Han Liu

Robust Classification Under Sample Selection Bias

Anqi Liu, Brian Ziebart

Improved Multimodal Deep Learning with Variation of Information

Kihyuk Sohn, Wenling Shang, Honglak Lee

Distributed Power-law Graph Computing: Theoretical and Empirical Analysis

Cong Xie, Ling Yan, Wu-jun Li, Zhihua Zhang

Deep Joint Task Learning for Generic Object Extraction

Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, Wangmeng Zuo

LSDA: Large Scale Detection through Adaptation

Judy Hoffman, Sergio Guadarrama, Eric S Tzeng, Ronghang Hu, Jeff Donahue

Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations

Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang

Deep Convolutional Neural Network for Image Deconvolution

Li Xu, Jimmy SJ Ren, Ce Liu, Jiaya Jia

QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models

Cho-jui Hsieh, Inderjit S Dhillon, Pradeep K Ravikumar, Stephen Becker, Peder A. Olsen

On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures

Harikrishna Narasimhan, Rohit Vaish, Shivani Agarwal

Algorithms for CVaR Optimization in MDPs

Yinlam Chow, Mohammad Ghavamzadeh

Sparse Space-Time Deconvolution for Calcium Image Analysis

Ferran Diego Andilla, Fred A. Hamprecht

Unsupervised Deep Haar Scattering on Graphs

Xu Chen, Xiuyuan Cheng, Stephane Mallat

Decoupled Variational Gaussian Inference

Mohammad Emtiyaz Khan

Hardness of parameter estimation in graphical models

Guy Bresler, David Gamarnik, Devavrat Shah

Large-Margin Convex Polytope Machine

Alex Kantchelian, Michael C Tschantz, Ling Huang, Anthony D Joseph, J. D. Tygar

PEWA: Patch-based Exponentially Weighted Aggregation for image denoising

Charles Kervrann

large scale canonical correlation analysis with iterative least squares

Yichao Lu, Dean P. Foster

Learning Multiple Tasks in Parallel with a Shared Annotator

Haim Cohen, Koby Crammer

Low-Rank Time-Frequency Synthesis

Cédric Févotte, Matthieu Kowalski

Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces

Minh Ha Quang, Marco San Biagio, Vittorio Murino

Deep Learning Face Representation by Joint Identification-Verification

Yi Sun, Yuheng Chen, Xiaogang Wang, Xiaoou Tang

Latent Support Measure Machines for Bag-of-Words Data Classification

Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada

Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology

Remi Lemonnier, Kevin Scaman, Nicolas Vayatis

An Autoencoder Approach to Learning Bilingual Word Representations

Sarath Chandar A P, Stanislas Lauly, Hugo Larochelle, Mitesh Khapra, Balaraman Ravindran

Augur: Data-Parallel Probabilistic Modeling

Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam C Pocock, Stephen Green

Stochastic Proximal Gradient Descent with Acceleration Techniques

Atsushi Nitanda

Rates of Convergence for Nearest Neighbor Classification

Kamalika Chaudhuri, Sanjoy Dasgupta

Convex Deep Learning via Normalized Kernels

Özlem Aslan, Xinhua Zhang, Dale Schuurmans

Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain

Charles Y Zheng, Franco Pestilli, Ariel Rokem

Transportability from Multiple Environments with Limited Experiments: Completeness Results

Elias Bareinboim, Judea Pearl

Real-Time Decoding of an Integrate and Fire Encoder

Shreya Saxena, Munther Dahleh

On Communication Cost of Distributed Statistical Estimation and Dimensionality

Ankit Garg, Tengyu Ma, Huy Nguyen

Recovery of Coherent Data via Low-Rank Dictionary Pursuit

Guangcan Liu, Ping Li

Multivariate Regression with Calibration

Han Liu, Lie Wang, Tuo Zhao

Causal Strategic Inference in Networked Microfinance Economies

Mohammad T Irfan, Luis E. Ortiz

Feedforward Learning of Mixture Models

Matthew Lawlor, Steven W Zucker

Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision

Deepti Pachauri, Risi Kondor, Gautam Sargur, Vikas Singh

Causal Inference through a Witness Protection Program

Ricardo Silva, Robin Evans

Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models

Michalis Titsias RC AUEB, Christopher Yau

A Drifting-Games Analysis for Online Learning and Applications to Boosting

Haipeng Luo, Robert E. Schapire

Feature Cross-Substitution in Adversarial Classification

Bo Li, Yevgeniy Vorobeychik

On Sparse Gaussian Chain Graph Models

Calvin McCarter, Seyoung Kim

Spectral Methods for Indian Buffet Process Inference

Hsiao-Yu Tung, Alexander J Smola

Deep Networks with Internal Selective Attention through Feedback Connections

Marijn F Stollenga, Jonathan Masci, Faustino Gomez, Jürgen Schmidhuber

Controlling privacy in recommender systems

Yu Xin, Tommi Jaakkola

Flexible Transfer Learning under Support and Model Shift

Xuezhi Wang, Jeff Schneider

On Multiplicative Multitask Feature Learning

Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun

Top Rank Optimization in Linear Time

Nan Li, Rong Jin, Zhi-Hua Zhou

Provable Submodular Minimization using Wolfe's Algorithm

Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari

Efficient Structured Matrix Rank Minimization

Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime Carbonell, Suvrit Sra

Weighted importance sampling for off-policy learning with linear function approximation

A. Rupam Mahmood, Hado P van Hasselt, Richard S. Sutton

Exponential Concentration of a Density Functional Estimator

Shashank Singh, Barnabás Póczos

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus

A Framework for Testing Identifiability of Bayesian Models of Perception

Luigi Acerbi, Wei Ji Ma, Sethu Vijayakumar

Learning Deep Features for Scene Recognition using Places Database

Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva

Mondrian Forests: Efficient Online Random Forests

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

On Model Parallelization and Scheduling Strategies for Distributed Machine Learning

Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A Gibson

Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data

Karthika Mohan, Judea Pearl

Probabilistic low-rank matrix completion on finite alphabets

Jean Lafond, Olga Klopp, Eric Moulines, Joseph Salmon

Efficient Minimax Strategies for Square Loss Games

Wouter M. Koolen, Alan Malek, Peter L. Bartlett, Peter L Bartlett

Low Rank Approximation Lower Bounds in Row-Update Streams

David Woodruff

Extreme bandits

Alexandra Carpentier, Michal Valko

Compressive Sensing of Signals from a GMM with Sparse Precision Matrices

Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin

Dimensionality Reduction with Subspace Structure Preservation

Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju

Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks

Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S. Shawe-taylor

Sequential Monte Carlo for Graphical Models

Christian Andersson Naesseth, Fredrik Lindsten, Thomas B Schön

Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs

David I Inouye, Pradeep K Ravikumar, Inderjit S Dhillon

Exact Post Model Selection Inference for Marginal Screening

Jason Lee, Jonathan E Taylor

Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization

Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang

Covariance shrinkage for autocorrelated data

Daniel Bartz, Klaus-Robert Müller

RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning

Marek Petrik, Dharmashankar Subramanian

SerialRank: Spectral Ranking using Seriation

Fajwel Fogel, Alexandre D'aspremont, Milan Vojnovic

Difference of Convex Functions Programming for Reinforcement Learning

Bilal Piot, Matthieu Geist, Olivier Pietquin

Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning

Brendan Mcmahan, Matthew Streeter

Near-optimal Reinforcement Learning in Factored MDPs

Ian Osband, Benjamin Van Roy

Extracting Latent Structure From Multiple Interacting Neural Populations

Joao Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu

Conditional Swap Regret and Conditional Correlated Equilibrium

Mehryar Mohri, Scott Yang

Optimal prior-dependent neural population codes under shared input noise

Agnieszka Grabska-Barwinska, Jonathan W Pillow

A Complete Variational Tracker

Ryan D Turner, Steven Bottone, Bhargav Avasarala

Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers

Bruno Conejo, Nikos Komodakis, Sebastien Leprince, Jean Philippe Avouac

Distance-Based Network Recovery under Feature Correlation

David Adametz, Volker Roth

Beyond Disagreement-Based Agnostic Active Learning

Chicheng Zhang, Kamalika Chaudhuri

Kernel Mean Estimation via Spectral Filtering

Krikamol Muandet, Bharath Sriperumbudur, Bernhard Schölkopf

Dependent nonparametric trees for dynamic hierarchical clustering

Kumar Avinava Dubey, Qirong Ho, Sinead A Williamson, Eric P. Xing, Eric P Xing

Optimizing Energy Production Using Policy Search and Predictive State Representations

Yuri Grinberg, Doina Precup, Michel Gendreau

Online Optimization for Max-Norm Regularization

Jie Shen, Huan Xu, Ping Li

Sparse PCA via Covariance Thresholding

Yash Deshpande, Andrea Montanari

Learning convolution filters for inverse covariance estimation of neural network connectivity

George Mohler

Magnitude-sensitive preference formation`

Nisheeth Srivastava, Ed Vul, Paul R. Schrater

Online combinatorial optimization with stochastic decision sets and adversarial losses

Gergely Neu, Michal Valko

Dynamic Rank Factor Model for Text Streams

Shaobo Han, Lin Du, Esther Salazar, Lawrence Carin