AISTATS 2012

157 papers

Stochastic Bandit Based on Empirical Moments

Junya Honda, Akimichi Takemura

Minh Hoai, Fernando De la Torre

Philipp Hennig, David Stern, Ralf Herbrich, Thore Graepel

Exchangeability Characterizes Optimality of Sequential Normalized Maximum Likelihood and Bayesian Prediction with Jeffreys Prior

Fares Hedayati, Peter Bartlett

Information Theoretic Model Validation for Spectral Clustering

Morteza Haghir Chehreghani, Alberto Giovanni Busetto, Joachim M. Buhmann

Evaluation of marginal likelihoods via the density of states

Michael Habeck

Locality Preserving Feature Learning

Quanquan Gu, Marina Danilevsky, Zhenhui Li, Jiawei Han

Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters

Marco Grzegorzyk, Dirk Husmeier

SpeedBoost: Anytime Prediction with Uniform Near-Optimality

Alex Grubb, Drew Bagnell

On Average Reward Policy Evaluation in Infinite-State Partially Observable Systems

Yuri Grinberg, Doina Precup

Scalable Inference on Kingman’s Coalescent using Pair Similarity

Dilan Gorur, Levi Boyles, Max Welling

Regularization Paths with Guarantees for Convex Semidefinite Optimization

Joachim Giesen, Martin Jaggi, Soeren Laue

UPAL: Unbiased Pool Based Active Learning

Ravi Ganti, Alexander Gray

Hierarchical Latent Dictionaries for Models of Brain Activation

Alona Fyshe, Emily Fox, David Dunson, Tom Mitchell

Factorized Asymptotic Bayesian Inference for Mixture Modeling

Ryohei Fujimaki, Satoshi Morinaga

Semiparametric Pseudo-Likelihood Estimation in Markov Random Fields

Antonino Freno

No Internal Regret via Neighborhood Watch

Dean Foster, Alexander Rakhlin

High-Rank Matrix Completion

Brian Eriksson, Laura Balzano, Robert Nowak

Statistical test for consistent estimation of causal effects in linear non-Gaussian models

Doris Entner, Patrik Hoyer, Peter Spirtes

Lightning-speed Structure Learning of Nonlinear Continuous Networks

Gal Elidan

Copula Network Classifiers (CNCs)

Gal Elidan

Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space

Robert Durrant, Ata Kaban

Lifted coordinate descent for learning with trace-norm regularization

Miroslav Dudík, Zaïd Harchaoui, Jérôme Malick

Generic Methods for Optimization-Based Modeling

Justin Domke

A metric learning perspective of SVM: on the relation of LMNN and SVM

Huyen Do, Alexandros Kalousis, Jun Wang, Adam Woznica

Deterministic Annealing for Semi-Supervised Structured Output Learning

Paramveer Dhillon, Sathiya Keerthi, Kedar Bellare, Olivier Chapelle, SUNDARARAJAN SELLAMANICKAM

There’s a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems

Ofer Dekel, Ohad Shamir

Protocols for Learning Classifiers on Distributed Data

Hal Daume III, Jeff Phillips, Avishek Saha, Suresh Venkatasubramanian

Hierarchical Relative Entropy Policy Search

Christian Daniel, Gerhard Neumann, Jan Peters

Wilks’ phenomenon and penalized likelihood-ratio test for nonparametric curve registration

Arnak Dalalyan, Olivier Collier

Gaussian Processes for time-marked time-series data

John Cunningham, Zoubin Ghahramani, Carl Rasmussen

Fast, Exact Model Selection and Permutation Testing for l2-Regularized Logistic Regression

Bryan Conroy, Paul Sajda

Minimax hypothesis testing for curve registration

Olivier Collier

Online Clustering with Experts

Anna Choromanska, Claire Monteleoni

Classifier Cascade for Minimizing Feature Evaluation Cost

Minmin Chen, Zhixiang Xu, Kilian Weinberger, Olivier Chapelle, Dor Kedem

A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping

Xiaohui Chen, Xinghua Shi, Xing Xu, Zhiyong Wang, Ryan Mills

Structured Sparse Canonical Correlation Analysis

Xi Chen, Liu Han, Jaime Carbonell

Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit

Alexandra Carpentier, Rémi Munos

Optimistic planning for Markov decision processes

Lucian Busoniu, Rémi Munos

On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models

David Buchman, Mark Schmidt, Shakir Mohamed, David Poole, Nando de Freitas

A Family of MCMC Methods on Implicitly Defined Manifolds

Marcus Brubaker, Mathieu Salzmann, Raquel Urtasun

Sample Complexity of Composite Likelihood

Joseph Bradley, Carlos Guestrin

Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing

Antoine Bordes, Xavier Glorot, Jason Weston, Yoshua Bengio

History-alignment models for bias-aware prediction of virological response to HIV combination therapy

Jasmina Bogojeska, Daniel Stockel, Maurizio Zazzi, Rolf Kaiser, Francesca Incardona

Wei Bian, Bo Xie, Dacheng Tao

Controlling Selection Bias in Causal Inference

Elias Bareinboim, Judea Pearl

Adaptive Metropolis with Online Relabeling

Rémi Bardenet, Olivier Cappe, Gersende Fort, Balázs Kégl

A General Framework for Structured Sparsity via Proximal Optimization

Luca Baldassarre, Jean Morales, Andreas Argyriou, Massimiliano Pontil

Scalable Personalization of Long-Term Physiological Monitoring: Active Learning Methodologies for Epileptic Seizure Onset Detection

Guha Balakrishnan, Zeeshan Syed

Minimax rates for homology inference

Sivaraman Balakrishnan, Alesandro Rinaldo, Don Sheehy, Aarti Singh, Larry Wasserman

Graphlet decomposition of a weighted network

Hossein Azari Soufiani, Edo Airoldi

Memory-efficient inference in dynamic graphical models using multiple cores

Galen Andrew, Jeff Bilmes

Factorized Diffusion Map Approximation

Saeed Amizadeh, Hamed Valizadegan, Milos Hauskrecht

Sparse Higher-Order Principal Components Analysis

Genevera Allen

Contextual Bandit Learning with Predictable Rewards

Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert Schapire

Discriminative Mixtures of Sparse Latent Fields for Risk Management

Felix Agakov, Peter Orchard, Amos Storkey

Online-to-Confidence-Set Conversions and Application to Sparse Stochastic Bandits

Yasin Abbasi-Yadkori, Dávid Pál, Csaba Szepesvári