NeurIPS 2011

306 papers

Sparse Features for PCA-Like Linear Regression

Christos Boutsidis, Petros Drineas, Malik Magdon-Ismail

A Machine Learning Approach to Predict Chemical Reactions

Matthew A. Kayala, Pierre F. Baldi

Statistical Performance of Convex Tensor Decomposition

Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima

Statistical Tests for Optimization Efficiency

Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling

Variational Gaussian Process Dynamical Systems

Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence

Generalized Beta Mixtures of Gaussians

Artin Armagan, Merlise Clyde, David B. Dunson

Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation

Nico Goernitz, Christian K. Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch

Greedy Model Averaging

Dong Dai, Tong Zhang

High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity

Po-ling Loh, Martin J. Wainwright

Speedy Q-Learning

Mohammad Ghavamzadeh, Hilbert J. Kappen, Mohammad G. Azar, Rémi Munos

Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning

Taiji Suzuki

On the Universality of Online Mirror Descent

Nati Srebro, Karthik Sridharan, Ambuj Tewari

Gaussian Process Training with Input Noise

Andrew Mchutchon, Carl E. Rasmussen

Structural equations and divisive normalization for energy-dependent component analysis

Jun-ichiro Hirayama, Aapo Hyvärinen

Confidence Sets for Network Structure

David S. Choi, Patrick J. Wolfe, Edoardo M. Airoldi

Efficient inference in matrix-variate Gaussian models with \iid observation noise

Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten Borgwardt

Learning person-object interactions for action recognition in still images

Vincent Delaitre, Josef Sivic, Ivan Laptev

On Learning Discrete Graphical Models using Greedy Methods

Ali Jalali, Christopher C. Johnson, Pradeep K Ravikumar, Pradeep K. Ravikumar

Clustered Multi-Task Learning Via Alternating Structure Optimization

Jiayu Zhou, Jianhui Chen, Jieping Ye

Select and Sample - A Model of Efficient Neural Inference and Learning

Jacquelyn A. Shelton, Abdul S. Sheikh, Pietro Berkes, Joerg Bornschein, Jörg Lücke

Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction

Siwei Lyu

Robust Lasso with missing and grossly corrupted observations

Nasser M. Nasrabadi, Trac D. Tran, Nam Nguyen

On U-processes and clustering performance

Stéphan J. Clémençcon

Infinite Latent SVM for Classification and Multi-task Learning

Jun Zhu, Ning Chen, Eric P. Xing

A Global Structural EM Algorithm for a Model of Cancer Progression

Ali Tofigh, Erik Sj̦lund, Mattias H̦glund, Jens Lagergren

The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning

Marius Kloft, Gilles Blanchard

Learning large-margin halfspaces with more malicious noise

Phil Long, Rocco Servedio

Higher-Order Correlation Clustering for Image Segmentation

Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo

Semantic Labeling of 3D Point Clouds for Indoor Scenes

Hema S. Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena

A Reinforcement Learning Theory for Homeostatic Regulation

Mehdi Keramati, Boris S. Gutkin

Selecting the State-Representation in Reinforcement Learning

Odalric-ambrym Maillard, Daniil Ryabko, Rémi Munos

Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices

Xianxing Zhang, Lawrence Carin, David B. Dunson

Structured Learning for Cell Tracking

Xinghua Lou, Fred A. Hamprecht

Learning to Agglomerate Superpixel Hierarchies

Viren Jain, Srinivas C. Turaga, K Briggman, Moritz N. Helmstaedter, Winfried Denk

Co-Training for Domain Adaptation

Minmin Chen, Kilian Q. Weinberger, John Blitzer

Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization

Mark Schmidt, Nicolas L. Roux, Francis R. Bach

Information Rates and Optimal Decoding in Large Neural Populations

Kamiar R. Rad, Liam Paninski

Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators

Dominique C. Perrault-joncas, Marina Meila

Expressive Power and Approximation Errors of Restricted Boltzmann Machines

Guido F. Montufar, Johannes Rauh, Nihat Ay

Shallow vs. Deep Sum-Product Networks

Olivier Delalleau, Yoshua Bengio

Efficient anomaly detection using bipartite k-NN graphs

Kumar Sricharan, Alfred O. Hero

Inverting Grice's Maxims to Learn Rules from Natural Language Extractions

Mohammad S. Sorower, Janardhan R. Doppa, Walker Orr, Prasad Tadepalli, Thomas G. Dietterich

Boosting with Maximum Adaptive Sampling

Charles Dubout, Francois Fleuret

t-divergence Based Approximate Inference

Nan Ding, Yuan Qi, S.v.n. Vishwanathan

See the Tree Through the Lines: The Shazoo Algorithm

Fabio Vitale, Nicolò Cesa-bianchi, Claudio Gentile, Giovanni Zappella

The Fast Convergence of Boosting

Matus J. Telgarsky

Online Submodular Set Cover, Ranking, and Repeated Active Learning

Andrew Guillory, Jeff A. Bilmes

Finite Time Analysis of Stratified Sampling for Monte Carlo

Alexandra Carpentier, Rémi Munos

Algorithms for Hyper-Parameter Optimization

James S. Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl

Portmanteau Vocabularies for Multi-Cue Image Representation

Fahad S. Khan, Joost Weijer, Andrew D. Bagdanov, Maria Vanrell

On Tracking The Partition Function

Guillaume Desjardins, Yoshua Bengio, Aaron C. Courville

Analysis and Improvement of Policy Gradient Estimation

Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama

On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference

Guy Broeck

Prismatic Algorithm for Discrete D.C. Programming Problem

Yoshinobu Kawahara, Takashi Washio

Generalised Coupled Tensor Factorisation

Kenan Y. Yılmaz, Ali T. Cemgil, Umut Simsekli

From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models

Skander Mensi, Richard Naud, Wulfram Gerstner

Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation

Onur Dikmen, Cédric Févotte

Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity

Nir Ailon

On Strategy Stitching in Large Extensive Form Multiplayer Games

Richard G. Gibson, Duane Szafron

Accelerated Adaptive Markov Chain for Partition Function Computation

Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

Penalty Decomposition Methods for Rank Minimization

Yong Zhang, Zhaosong Lu

Practical Variational Inference for Neural Networks

Alex Graves

Robust Multi-Class Gaussian Process Classification

Daniel Hernández-lobato, José M. Hernández-lobato, Pierre Dupont

Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness

Rémi Munos

Predicting Dynamic Difficulty

Olana Missura, Thomas Gärtner

Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint

Kenji Fukumizu, Gert R. Lanckriet, Bharath K. Sriperumbudur

Convergent Fitted Value Iteration with Linear Function Approximation

Daniel J. Lizotte

Sequence learning with hidden units in spiking neural networks

Johanni Brea, Walter Senn, Jean-pascal Pfister

Predicting response time and error rates in visual search

Bo Chen, Vidhya Navalpakkam, Pietro Perona

Multi-armed bandits on implicit metric spaces

Aleksandrs Slivkins

Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment

Sebastian A. Kurtek, Anuj Srivastava, Wei Wu

Exploiting spatial overlap to efficiently compute appearance distances between image windows

Bogdan Alexe, Viviana Petrescu, Vittorio Ferrari

An Exact Algorithm for F-Measure Maximization

Krzysztof J. Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

Empirical models of spiking in neural populations

Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy

Inductive reasoning about chimeric creatures

Charles Kemp

Object Detection with Grammar Models

Ross B. Girshick, Pedro F. Felzenszwalb, David A. McAllester

Learning to Learn with Compound HD Models

Antonio Torralba, Joshua B. Tenenbaum, Ruslan Salakhutdinov

Projection onto A Nonnegative Max-Heap

Jun Liu, Liang Sun, Jieping Ye

Multi-View Learning of Word Embeddings via CCA

Paramveer Dhillon, Dean P. Foster, Lyle H. Ungar

Learning Auto-regressive Models from Sequence and Non-sequence Data

Tzu-kuo Huang, Jeff G. Schneider

Selecting Receptive Fields in Deep Networks

Adam Coates, Andrew Y. Ng

Active Ranking using Pairwise Comparisons

Kevin G. Jamieson, Robert Nowak

Maximum Margin Multi-Label Structured Prediction

Christoph H. Lampert

Purushottam Kar, Prateek Jain

Online Learning: Stochastic, Constrained, and Smoothed Adversaries

Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari

Stochastic convex optimization with bandit feedback

Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin

Continuous-Time Regression Models for Longitudinal Networks

Duy Q. Vu, David Hunter, Padhraic Smyth, Arthur U. Asuncion

Matrix Completion for Multi-label Image Classification

Ricardo S. Cabral, Fernando Torre, Joao P. Costeira, Alexandre Bernardino

Adaptive Hedge

Tim V. Erven, Wouter M. Koolen, Steven D. Rooij, Peter Grünwald

Bayesian Spike-Triggered Covariance Analysis

ll Memming Park, Jonathan W Pillow, Jonathan W. Pillow

Inferring spike-timing-dependent plasticity from spike train data

Ian Stevenson, Konrad Koerding

Beating SGD: Learning SVMs in Sublinear Time

Elad Hazan, Tomer Koren, Nati Srebro

Improving Topic Coherence with Regularized Topic Models

David Newman, Edwin V. Bonilla, Wray Buntine

Uniqueness of Belief Propagation on Signed Graphs

Yusuke Watanabe

Modelling Genetic Variations using Fragmentation-Coagulation Processes

Yee W. Teh, Charles Blundell, Lloyd Elliott

Im2Text: Describing Images Using 1 Million Captioned Photographs

Vicente Ordonez, Girish Kulkarni, Tamara L. Berg

Non-conjugate Variational Message Passing for Multinomial and Binary Regression

David A. Knowles, Tom Minka

Divide-and-Conquer Matrix Factorization

Lester W. Mackey, Michael I. Jordan, Ameet Talwalkar

Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition

Jia Deng, Sanjeev Satheesh, Alexander C. Berg, Fei Li

PAC-Bayesian Analysis of Contextual Bandits

Yevgeny Seldin, Peter Auer, John S. Shawe-taylor, Ronald Ortner, François Laviolette