NeurIPS 2010

292 papers

Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors

Alessandro Chiuso, Gianluigi Pillonetto

Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition

Serhat Bucak, Rong Jin, Anil K. Jain

Robust PCA via Outlier Pursuit

Huan Xu, Constantine Caramanis, Sujay Sanghavi

Learning To Count Objects in Images

Victor Lempitsky, Andrew Zisserman

Fast detection of multiple change-points shared by many signals using group LARS

Jean-philippe Vert, Kevin Bleakley

An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA

Matthias Hein, Thomas Bühler

Learning invariant features using the Transformed Indian Buffet Process

Joseph L. Austerweil, Thomas L. Griffiths

Copula Processes

Andrew G Wilson, Zoubin Ghahramani

Probabilistic Inference and Differential Privacy

Oliver Williams, Frank Mcsherry

Distributed Dual Averaging In Networks

Alekh Agarwal, Martin J. Wainwright, John C. Duchi

Implicit encoding of prior probabilities in optimal neural populations

Deep Ganguli, Eero P. Simoncelli

Towards Property-Based Classification of Clustering Paradigms

Margareta Ackerman, Shai Ben-David, David Loker

Layered image motion with explicit occlusions, temporal consistency, and depth ordering

Deqing Sun, Erik B. Sudderth, Michael J. Black

The LASSO risk: asymptotic results and real world examples

Mohsen Bayati, José Pereira, Andrea Montanari

Functional form of motion priors in human motion perception

Hongjing Lu, Tungyou Lin, Alan Lee, Luminita Vese, Alan L. Yuille

Active Instance Sampling via Matrix Partition

Yuhong Guo

Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models

Felipe Gerhard, Wulfram Gerstner

Divisive Normalization: Justification and Effectiveness as Efficient Coding Transform

Siwei Lyu

Spatial and anatomical regularization of SVM for brain image analysis

Remi Cuingnet, Marie Chupin, Habib Benali, Olivier Colliot

Monte-Carlo Planning in Large POMDPs

David Silver, Joel Veness

Learning via Gaussian Herding

Koby Crammer, Daniel D. Lee

Learning the context of a category

Dan Navarro

Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models

Congcong Li, Adarsh Kowdle, Ashutosh Saxena, Tsuhan Chen

Inference with Multivariate Heavy-Tails in Linear Models

Danny Bickson, Carlos Guestrin

Batch Bayesian Optimization via Simulation Matching

Javad Azimi, Alan Fern, Xiaoli Z. Fern

Multi-Stage Dantzig Selector

Ji Liu, Peter Wonka, Jieping Ye

Learning Efficient Markov Networks

Vibhav Gogate, William Webb, Pedro Domingos

Self-Paced Learning for Latent Variable Models

M. P. Kumar, Benjamin Packer, Daphne Koller

Lower Bounds on Rate of Convergence of Cutting Plane Methods

Xinhua Zhang, Ankan Saha, S.v.n. Vishwanathan

A Discriminative Latent Model of Image Region and Object Tag Correspondence

Yang Wang, Greg Mori

Mixture of time-warped trajectory models for movement decoding

Elaine Corbett, Eric Perreault, Konrad Koerding

Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable

Lauren Hannah, Warren Powell, David M. Blei

Variable margin losses for classifier design

Hamed Masnadi-shirazi, Nuno Vasconcelos

Online Learning: Random Averages, Combinatorial Parameters, and Learnability

Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari

Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions

Achintya Kundu, Vikram Tankasali, Chiranjib Bhattacharyya, Aharon Ben-tal

A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction

Tamir Hazan, Raquel Urtasun

Word Features for Latent Dirichlet Allocation

James Petterson, Wray Buntine, Shravan M. Narayanamurthy, Tibério S. Caetano, Alex J. Smola

Brain covariance selection: better individual functional connectivity models using population prior

Gael Varoquaux, Alexandre Gramfort, Jean-baptiste Poline, Bertrand Thirion

Probabilistic Deterministic Infinite Automata

David Pfau, Nicholas Bartlett, Frank Wood

Large-Scale Matrix Factorization with Missing Data under Additional Constraints

Kaushik Mitra, Sameer Sheorey, Rama Chellappa

Active Learning Applied to Patient-Adaptive Heartbeat Classification

Jenna Wiens, John V. Guttag

Inductive Regularized Learning of Kernel Functions

Prateek Jain, Brian Kulis, Inderjit S Dhillon, Inderjit S. Dhillon

Active Estimation of F-Measures

Christoph Sawade, Niels Landwehr, Tobias Scheffer

Empirical Bernstein Inequalities for U-Statistics

Thomas Peel, Sandrine Anthoine, Liva Ralaivola

Approximate inference in continuous time Gaussian-Jump processes

Manfred Opper, Andreas Ruttor, Guido Sanguinetti

Synergies in learning words and their referents

Mark Johnson, Katherine Demuth, Bevan Jones, Michael J. Black

Pose-Sensitive Embedding by Nonlinear NCA Regression

Graham W. Taylor, Rob Fergus, George Williams, Ian Spiro, Christoph Bregler

Moreau-Yosida Regularization for Grouped Tree Structure Learning

Jun Liu, Jieping Ye

Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model

Peggy Series, David P. Reichert, Amos J. Storkey

Gaussian sampling by local perturbations

George Papandreou, Alan L. Yuille

Global seismic monitoring as probabilistic inference

Nimar Arora, Stuart Russell, Paul Kidwell, Erik B. Sudderth

On the Convexity of Latent Social Network Inference

Seth Myers, Jure Leskovec

Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

Kaiming Li, Lei Guo, Carlos Faraco, Dajiang Zhu, Fan Deng

Avoiding False Positive in Multi-Instance Learning

Yanjun Han, Qing Tao, Jue Wang

Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development

Diane Hu, Laurens Maaten, Youngmin Cho, Sorin Lerner, Lawrence K. Saul

Variational Inference over Combinatorial Spaces

Alexandre Bouchard-côté, Michael I. Jordan

Direct Loss Minimization for Structured Prediction

Tamir Hazan, Joseph Keshet, David A. McAllester

Learning from Candidate Labeling Sets

Jie Luo, Francesco Orabona

Two-Layer Generalization Analysis for Ranking Using Rademacher Average

Wei Chen, Tie-yan Liu, Zhi-ming Ma

Probabilistic latent variable models for distinguishing between cause and effect

Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M. Mooij, Bernhard Schölkopf

Accounting for network effects in neuronal responses using L1 regularized point process models

Ryan Kelly, Matthew Smith, Robert Kass, Tai S. Lee

On the Theory of Learnining with Privileged Information

Dmitry Pechyony, Vladimir Vapnik

Nonparametric Bayesian Policy Priors for Reinforcement Learning

Finale Doshi-velez, David Wingate, Nicholas Roy, Joshua B. Tenenbaum

A Novel Kernel for Learning a Neuron Model from Spike Train Data

Nicholas Fisher, Arunava Banerjee

Basis Construction from Power Series Expansions of Value Functions

Sridhar Mahadevan, Bo Liu

Parametric Bandits: The Generalized Linear Case

Sarah Filippi, Olivier Cappe, Aurélien Garivier, Csaba Szepesvári

Learning from Logged Implicit Exploration Data

Alex Strehl, John Langford, Lihong Li, Sham M. Kakade

Efficient Relational Learning with Hidden Variable Detection

Ni Lao, Jun Zhu, Liu Liu, Yandong Liu, William W. Cohen

Reverse Multi-Label Learning

James Petterson, Tibério S. Caetano

Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference

Vikas Sindhwani, Aurelie C. Lozano

Sparse Coding for Learning Interpretable Spatio-Temporal Primitives

Taehwan Kim, Gregory Shakhnarovich, Raquel Urtasun

Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine

George Dahl, Marc'aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton

An Approximate Inference Approach to Temporal Optimization in Optimal Control

Konrad Rawlik, Marc Toussaint, Sethu Vijayakumar

Throttling Poisson Processes

Uwe Dick, Peter Haider, Thomas Vanck, Michael Brückner, Tobias Scheffer

Rates of convergence for the cluster tree

Kamalika Chaudhuri, Sanjoy Dasgupta

Optimal learning rates for Kernel Conjugate Gradient regression

Gilles Blanchard, Nicole Krämer

Robust Clustering as Ensembles of Affinity Relations

Hairong Liu, Longin J. Latecki, Shuicheng Yan

CUR from a Sparse Optimization Viewpoint

Jacob Bien, Ya Xu, Michael W. Mahoney

Multitask Learning without Label Correspondences

Novi Quadrianto, James Petterson, Tibério S. Caetano, Alex J. Smola, S.v.n. Vishwanathan

Unsupervised Kernel Dimension Reduction

Meihong Wang, Fei Sha, Michael I. Jordan

Random Walk Approach to Regret Minimization

Hariharan Narayanan, Alexander Rakhlin

Approximate Inference by Compilation to Arithmetic Circuits

Daniel Lowd, Pedro Domingos

MAP estimation in Binary MRFs via Bipartite Multi-cuts

Sashank J. Reddi, Sunita Sarawagi, Sundar Vishwanathan

Shadow Dirichlet for Restricted Probability Modeling

Bela Frigyik, Maya Gupta, Yihua Chen

Parallelized Stochastic Gradient Descent

Martin Zinkevich, Markus Weimer, Lihong Li, Alex J. Smola

Joint Cascade Optimization Using A Product Of Boosted Classifiers

Leonidas Lefakis, Francois Fleuret

An analysis on negative curvature induced by singularity in multi-layer neural-network learning

Eiji Mizutani, Stuart Dreyfus

Feature Construction for Inverse Reinforcement Learning

Sergey Levine, Zoran Popovic, Vladlen Koltun

LSTD with Random Projections

Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric Maillard, Rémi Munos

Tree-Structured Stick Breaking for Hierarchical Data

Zoubin Ghahramani, Michael I. Jordan, Ryan P. Adams

Structural epitome: a way to summarize one’s visual experience

Nebojsa Jojic, Alessandro Perina, Vittorio Murino

A unified model of short-range and long-range motion perception

Shuang Wu, Xuming He, Hongjing Lu, Alan L. Yuille

Factorized Latent Spaces with Structured Sparsity

Yangqing Jia, Mathieu Salzmann, Trevor Darrell

Static Analysis of Binary Executables Using Structural SVMs

Nikos Karampatziakis

Segmentation as Maximum-Weight Independent Set

William Brendel, Sinisa Todorovic

Multiple Kernel Learning and the SMO Algorithm

Zhaonan Sun, Nawanol Ampornpunt, Manik Varma, S.v.n. Vishwanathan

Learning Convolutional Feature Hierarchies for Visual Recognition

Koray Kavukcuoglu, Pierre Sermanet, Y-lan Boureau, Karol Gregor, Michael Mathieu

Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication

Guy Isely, Christopher Hillar, Fritz Sommer

Practical Large-Scale Optimization for Max-norm Regularization

Jason Lee, Ben Recht, Nathan Srebro, Joel Tropp, Ruslan Salakhutdinov

Construction of Dependent Dirichlet Processes based on Poisson Processes

Dahua Lin, Eric Grimson, John W. Fisher