AISTATS 2010

125 papers

Exclusive Lasso for Multi-task Feature Selection

Yang Zhou, Rong Jin, Steven Chu–Hong Hoi

Matrix-Variate Dirichlet Process Mixture Models

Zhihua Zhang, Guang Dai, Michael I. Jordan

Bayesian Generalized Kernel Models

Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan

Multi-Task Learning using Generalized t Process

Yu Zhang, Dit–Yan Yeung

Bayesian Online Learning for Multi-label and Multi-variate Performance Measures

Xinhua Zhang, Thore Graepel, Ralf Herbrich

Risk Bounds for Levy Processes in the PAC-Learning Framework

Chao Zhang, Dacheng Tao

A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra

Ji Won Yoon, Simon Wilson, K. Hun Mok

Modeling annotator expertise: Learning when everybody knows a bit of something

Yan Yan, Rómer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo

Dependent Indian Buffet Processes

Sinead Williamson, Peter Orbanz, Zoubin Ghahramani

Structured Prediction Cascades

David Weiss, Benjamin Taskar

Online Passive-Aggressive Algorithms on a Budget

Zhuang Wang, Slobodan Vucetic

A Potential-based Framework for Online Multi-class Learning with Partial Feedback

Shijun Wang, Rong Jin, Hamed Valizadegan

An Alternative Prior Process for Nonparametric Bayesian Clustering

Hanna Wallach, Shane Jensen, Lee Dicker, Katherine Heller

Guarantees for Approximate Incremental SVMs

Nicolas Usunier, Antoine Bordes, Léon Bottou

Sequential Monte Carlo Samplers for Dirichlet Process Mixtures

Yener Ulker, Bilge Günsel, Taylan Cemgil

State-Space Inference and Learning with Gaussian Processes

Ryan Turner, Marc Deisenroth, Carl Rasmussen

Learning Causal Structure from Overlapping Variable Sets

Sofia Triantafillou, Ioannis Tsamardinos, Ioannis Tollis

A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping

Peter Torma, András György, Csaba Szepesvári

Bayesian Gaussian Process Latent Variable Model

Michalis Titsias, Neil D. Lawrence

Unsupervised Aggregation for Classification Problems with Large Numbers of Categories

Ivan Titov, Alexandre Klementiev, Kevin Small, Dan Roth

Learning Policy Improvements with Path Integrals

Evangelos Theodorou, Jonas Buchli, Stefan Schaal

Hartigan’s Method: k-means Clustering without Voronoi

Matus Telgarsky, Andrea Vattani

HOP-MAP: Efficient Message Passing with High Order Potentials

Daniel Tarlow, Inmar Givoni, Richard Zemel

Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation

Taiji Suzuki, Masashi Sugiyama

Inference and Learning in Networks of Queues

Charles Sutton, Michael I. Jordan

On the Convergence Properties of Contrastive Divergence

Ilya Sutskever, Tijmen Tieleman

Conditional Density Estimation via Least-Squares Density Ratio Estimation

Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya

On the relation between universality, characteristic kernels and RKHS embedding of measures

Bharath Sriperumbudur, Kenji Fukumizu, Gert Lanckriet

Nonparametric Tree Graphical Models

Le Song, Arthur Gretton, Carlos Guestrin

Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks

Nikolai Slavov

Detecting Weak but Hierarchically-Structured Patterns in Networks

Aarti Singh, Robert Nowak, Robert Calderbank

Reduced-Rank Hidden Markov Models

Sajid Siddiqi, Byron Boots, Geoffrey Gordon

Empirical Bernstein Boosting

Pannagadatta Shivaswamy, Tony Jebara

Dense Message Passing for Sparse Principal Component Analysis

Kevin Sharp, Magnus Rattray

Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching

Nic Schraudolph

Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials

Mark Schmidt, Kevin Murphy

Factorized Orthogonal Latent Spaces

Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, Trevor Darrell

Efficient Learning of Deep Boltzmann Machines

Ruslan Salakhutdinov, Hugo Larochelle

Reducing Label Complexity by Learning From Bags

Sivan Sabato, Nathan Srebro, Naftali Tishby

Active Sequential Learning with Tactile Feedback

Hannes Saal, Jo–Anne Ting, Sethu Vijayakumar

Approximate parameter inference in a stochastic reaction-diffusion model

Andreas Ruttor, Manfred Opper

Efficient Reductions for Imitation Learning

Stephane Ross, Drew Bagnell

A Regularization Approach to Nonlinear Variable Selection

Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Alessandro Verri, Silvia Villa

Gaussian processes with monotonicity information

Jaakko Riihimäki, Aki Vehtari

Convexity of Proper Composite Binary Losses

Mark Reid, Robert Williamson

Nonparametric prior for adaptive sparsity

Vikas Raykar, Linda Zhao

Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images

Marc’Aurelio Ranzato, Alex Krizhevsky, Geoffrey Hinton

Infinite Predictor Subspace Models for Multitask Learning

Piyush Rai, Hal Daume III

REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization

Barnabás Póczos, Sergey Kirshner, Csaba Szepesvári

Identifying Cause and Effect on Discrete Data using Additive Noise Models

Jonas Peters, Dominik Janzing, Bernhard Schölkopf

Bayesian structure discovery in Bayesian networks with less space

Pekka Parviainen, Mikko Koivisto

A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection

Silvia Pandolfi, Francesco Bartolucci, Nial Friel

Approximation of hidden Markov models by mixtures of experts with application to particle filtering

Jimmy Olsson, Jonas Ströjby

Fluid Dynamics Models for Low Rank Discriminant Analysis

Yung–Kyun Noh, Byoung–Tak Zhang, Daniel Lee

Incremental Sparsification for Real-time Online Model Learning

Duy Nguyen–Tuong, Jan Peters

Near-Optimal Evasion of Convex-Inducing Classifiers

Blaine Nelson, Benjamin Rubinstein, Ling Huang, Anthony Joseph, Shing–hon Lau

Elliptical slice sampling

Iain Murray, Ryan Adams, David MacKay

Discriminative Topic Segmentation of Text and Speech

Mehryar Mohri, Pedro Moreno, Eugene Weinstein

Exploiting Within-Clique Factorizations in Junction-Tree Algorithms

Julian McAuley, Tiberio Caetano

Parallelizable Sampling of Markov Random Fields

James Martens, Ilya Sutskever

Inductive Principles for Restricted Boltzmann Machine Learning

Benjamin Marlin, Kevin Swersky, Bo Chen, Nando Freitas

Supervised Dimension Reduction Using Bayesian Mixture Modeling

Kai Mao, Feng Liang, Sayan Mukherjee

Exploiting Feature Covariance in High-Dimensional Online Learning

Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence Saul

Contextual Multi-Armed Bandits

Tyler Lu, Dávid Pál, Martin Pal

Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net

Alexander Lorbert, David Eis, Victoria Kostina, David Blei, Peter Ramadge

Descent Methods for Tuning Parameter Refinement

Alexander Lorbert, Peter Ramadge

The Group Dantzig Selector

Han Liu, Jian Zhang, Xiaoye Jiang, Jun Liu

Simple Exponential Family PCA

Jun Li, Dacheng Tao

The Feature Selection Path in Kernel Methods

Fuxin Li, Cristian Sminchisescu

Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction

Guy Lever

Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms

Nevena Lazic, Brendan Frey, Parham Aarabi

Semi-Supervised Learning with Max-Margin Graph Cuts

Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang

Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach

Mladen Kolar, Eric Xing

Online Anomaly Detection under Adversarial Impact

Marius Kloft, Pavel Laskov

Fast Active-set-type Algorithms for l1-regularized Linear Regression

Jingu Kim, Haesun Park

Collaborative Filtering on a Budget

Alexandros Karatzoglou, Alex Smola, Markus Weimer

Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity

Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari

Nonlinear functional regression: a functional RKHS approach

Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Manuel Davy

Structured Sparse Principal Component Analysis

Rodolphe Jenatton, Guillaume Obozinski, Francis Bach

Learning Bayesian Network Structure using LP Relaxations

Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila

Learning Nonlinear Dynamic Models from Non-sequenced Data

Tzu–Kuo Huang, Le Song, Jeff Schneider

Maximum-likelihood learning of cumulative distribution functions on graphs

Jim Huang, Nebojsa Jojic

Collaborative Filtering via Rating Concentration

Bert Huang, Tony Jebara

Coherent Inference on Optimal Play in Game Trees

Philipp Hennig, David Stern, Thore Graepel

Negative Results for Active Learning with Convex Losses

Steve Hanneke, Liu Yang

Dirichlet Process Mixtures of Generalized Linear Models

Lauren Hannah, David Blei, Warren Powell

Boosted Optimization for Network Classification

Timothy Hancock, Hiroshi Mamitsuka

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Michael Gutmann, Aapo Hyvärinen

Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries

Shengbo Guo, Scott Sanner

Sufficient covariates and linear propensity analysis

Hui GUO, Philip Dawid

Regret Bounds for Gaussian Process Bandit Problems

Steffen Grünewälder, Jean–Yves Audibert, Manfred Opper, John Shawe–Taylor

Locally Linear Denoising on Image Manifolds

Dian Gong, Fei Sha, Gérard Medioni

On Combining Graph-based Variance Reduction schemes

Vibhav Gogate, Rina Dechter

Understanding the difficulty of training deep feedforward neural networks

Xavier Glorot, Yoshua Bengio

Variational methods for Reinforcement Learning

Thomas Furmston, David Barber

Posterior distributions are computable from predictive distributions

Cameron Freer, Daniel Roy

A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation

Florence Forbes, Senan Doyle, Daniel Garcia–Lorenzo, Christian Barillot, Michel Dojat

Model-Free Monte Carlo-like Policy Evaluation

Raphael Fonteneau, Susan Murphy, Louis Wehenkel, Damien Ernst

Semi-Supervised Learning via Generalized Maximum Entropy

Ayse Erkan, Yasemin Altun

Why Does Unsupervised Pre-training Help Deep Learning?

Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent