AISTATS 2011

106 papers

Two-Layer Multiple Kernel Learning

Jinfeng Zhuang, Ivor W. Tsang, Steven C.H. Hoi

Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning

Xueyuan Zhou, Nathan Srebro

Semi-supervised Learning by Higher Order Regularization

Xueyuan Zhou, Mikhail Belkin

Dependent Hierarchical Beta Process for Image Interpolation and Denoising

Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David Dunson, Lawrence Carin

Multi-Label Output Codes using Canonical Correlation Analysis

Yi Zhang, Jeff Schneider

Generalization Bound for Infinitely Divisible Empirical Process

Chao Zhang, Dacheng Tao

An Instantiation-Based Theorem Prover for First-Order Programming

Erik Zawadzki, Geoffrey Gordon, André Platzer

A Finite Newton Algorithm for Non-degenerate Piecewise Linear Systems

Xiao–Tong Yuan, Shuicheng Yan

Efficient variable selection in support vector machines via the alternating direction method of multipliers

Gui–Bo Ye, Yifei Chen, Xiaohui Xie

Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality

Shuang–Hong Yang, Steven P. Crain, Hongyuan Zha

The Sample Complexity of Self-Verifying Bayesian Active Learning

Liu Yang, Steve Hanneke, Jaime Carbonell

Cross-Domain Object Matching with Model Selection

Makoto Yamada, Masashi Sugiyama

Multicore Gibbs Sampling in Dense, Unstructured Graphs

Tianbing Xu, Alexander Ihler

Hierarchical Probabilistic Models for Group Anomaly Detection

Liang Xiong, Barnabás Póczos, Jeff Schneider, Andrew Connolly, Jake VanderPlas

Relational Learning with One Network: An Asymptotic Analysis

Rongjing Xiang, Jennifer Neville

Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation

David Wingate, Andreas Stuhlmueller, Noah Goodman

Information Theoretical Clustering via Semidefinite Programming

Meihong Wang, Fei Sha

Online Variational Inference for the Hierarchical Dirichlet Process

Chong Wang, John Paisley, David M. Blei

Active Boosted Learning (ActBoost)

Kirill Trapeznikov, Venkatesh Saligrama, David Castañón

Estimating Probabilities in Recommendation Systems

Mingxuan Sun, Guy Lebanon, Paul Kidwell

Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure

Veselin Stoyanov, Alexander Ropson, Jason Eisner

Machine Learning Markets

Amos Storkey

Kernel Belief Propagation

Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin

Spectral Chinese Restaurant Processes: Nonparametric Clustering Based on Similarities

Richard Socher, Andrew Maas, Christopher Manning

Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening

Ning Situ, Xiaojing Yuan, George Zouridakis

Asymptotic Theory for Linear-Chain Conditional Random Fields

Mathieu Sinn, Pascal Poupart

Mixed Cumulative Distribution Networks

Ricardo Silva, Charles Blundell, Yee Whye Teh

Spectral Clustering on a Budget

Ohad Shamir, Naftali Tishby

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Matthias Seeger, Hannes Nickisch

Online Learning of Multiple Tasks and Their Relationships

Avishek Saha, Piyush Rai, Hal DaumÃ© III, Suresh Venkatasubramanian

Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback

Ankan Saha, Ambuj Tewari

A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

Stephane Ross, Geoffrey Gordon, Drew Bagnell

On NDCG Consistency of Listwise Ranking Methods

Pradeep Ravikumar, Ambuj Tewari, Eunho Yang

On the Estimation of α-Divergences

Barnabás Póczos, Jeff Schneider

Directional Statistics on Permutations

Sergey M. Plis, Stephen McCracken, Terran Lane, Vince D. Calhoun

Faithfulness in Chain Graphs: The Gaussian Case

Jose M. Peña

Generative Modeling for Maximizing Precision and Recall in Information Visualization

Jaakko Peltonen, Samuel Kaski

Adaptive Bandits: Towards the best history-dependent strategy

Maillard Odalric, Rémi Munos

Maximum Volume Clustering

Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama

Dimensionality Reduction for Spectral Clustering

Donglin Niu, Jennifer Dy, Michael I. Jordan

TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents

Ramesh Nallapati, Daniel McFarland, Christopher Manning

Can matrix coherence be efficiently and accurately estimated?

Mehryar Mohri, Ameet Talwalkar

Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization

Brendan Mcmahan

Estimating beta-mixing coefficients

Daniel McDonald, Cosma Shalizi, Mark Schervish

Online Learning of Structured Predictors with Multiple Kernels

Andre Filipe Torres Martins, Noah Smith, Eric Xing, Pedro Aguiar, Mário Figueiredo

CAKE: Convex Adaptive Kernel Density Estimation

Ravi Sastry Ganti Mahapatruni, Alexander Gray

Learning mixtures of Gaussians with maximum-a-posteriori oracle

Satyaki Mahalanabis

Hidden-Unit Conditional Random Fields

Laurens van der Maaten, Max Welling, Lawrence Saul

Learning Class-relevant Features and Class-irrelevant Features via a Hybrid third-order RBM

Heng Luo, Ruimin Shen, Changyong Niu, Carsten Ullrich

A Fast Algorithm for Recovery of Jointly Sparse Vectors based on the Alternating Direction Methods

Hongtao Lu, Xianzhong Long, Jingyuan Lv

Group Orthogonal Matching Pursuit for Logistic Regression

Aurélie Lozano, Grzegorz Swirszcz, Naoki Abe

Bayesian Hierarchical Cross-Clustering

Dazhuo Li, Patrick Shafto

Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection

Bin Li, Steven C.H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan

Robust Bayesian Matrix Factorisation

Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau

Approximate inference for the loss-calibrated Bayesian

Simon Lacoste–Julien, Ferenc Huszar, Zoubin Ghahramani

On Time Varying Undirected Graphs

Mladen Kolar, Eric P. Xing

Convex envelopes of complexity controlling penalties: the case against premature envelopment

Vladimir Jojic, Suchi Saria, Daphne Koller

Convergent Decomposition Solvers for Tree-reweighted Free Energies

Jeremy Jancsary, Gerald Matz

On Learning Discrete Graphical Models using Group-Sparse Regularization

Ali Jalali, Pradeep Ravikumar, Vishvas Vasuki, Sujay Sanghavi

Improved Loss Bounds For Multiple Kernel Learning

Zakria Hussain, John Shawe–Taylor

Fast b-matching via Sufficient Selection Belief Propagation

Bert Huang, Tony Jebara

Optimal Distributed Market-Based Planning for Multi-Agent Systems with Shared Resources

Sue Ann Hong, Geoffrey Gordon

Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks

Qirong Ho, Le Song, Eric Xing

Multiscale Community Blockmodel for Network Exploration

Qirong Ho, Ankur Parikh, Le Song, Eric Xing

Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees

Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin

Deep Sparse Rectifier Neural Networks

Xavier Glorot, Antoine Bordes, Yoshua Bengio

Learning from positive and unlabeled examples by enforcing statistical significance

Pierre Geurts

Block-sparse Solutions using Kernel Block RIP and its Application to Group Lasso

Rahul Garg, Rohit Khandekar

A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks

James Foulds, Christopher DuBois, Arthur Asuncion, Carter Butts, Padhraic Smyth

Revisiting MAP Estimation, Message Passing and Perfect Graphs

James Foulds, Nicholas Navaroli, Padhraic Smyth, Alexander Ihler

A novel greedy algorithm for Nyström approximation

Ahmed Farahat, Ali Ghodsi, Mohamed Kamel

Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities

Brian Eriksson, Gautam Dasarathy, Aarti Singh, Rob Nowak

Bagged Structure Learning of Bayesian Network

Gal Elidan

Optimal and Robust Price Experimentation: Learning by Lottery

Christopher Dance, Onno Zoeter

A Spike and Slab Restricted Boltzmann Machine

Aaron Courville, James Bergstra, Yoshua Bengio

Deep Learning for Efficient Discriminative Parsing

Ronan Collobert

An Analysis of Single-Layer Networks in Unsupervised Feature Learning

Adam Coates, Andrew Ng, Honglak Lee

Contextual Bandits with Linear Payoff Functions

Wei Chu, Lihong Li, Lev Reyzin, Robert Schapire

Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models

Edward Challis, David Barber

Switch-Reset Models : Exact and Approximate Inference

Chris Bracegirdle, David Barber

Relative Entropy Inverse Reinforcement Learning

Abdeslam Boularias, Jens Kober, Jan Peters

Domain Adaptation with Coupled Subspaces

John Blitzer, Sham Kakade, Dean Foster

Deep Learners Benefit More from Out-of-Distribution Examples

Yoshua Bengio, Frédéric Bastien, Arnaud Bergeron, Nicolas Boulanger–Lewandowski, Thomas Breuel

Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach

Gowtham Bellala, Suresh Bhavnani, Clayton Scott

Tighter Relaxations for MAP-MRF Inference: A Local Primal-Dual Gap based Separation Algorithm

Dhruv Batra, Sebastian Nowozin, Pushmeet Kohli

Unsupervised Supervised Learning II: Margin-Based Classification without Labels

Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon

Statistical Optimization of Non-Negative Matrix Factorization

Anoop Korattikara Balan, Levi Boyles, Max Welling, Jingu Kim, Haesun Park

Dynamic Policy Programming with Function Approximation

Mohammad Gheshlaghi azar, Vicenç Gómez, Bert Kappen

Polytope samplers for inference in ill-posed inverse problems

Edoardo Airoldi, Bertrand Haas

Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text

Amr Ahmed, Qirong Ho, Choon Hui Teo, Jacob Eisenstein, Alex Smola

Linear-Time Estimators for Propensity Scores

Deepak Agarwal, Lihong Li, Alexander Smola

Generative Kernels for Exponential Families

Arvind Agarwal, Hal Daume III

Discussion of “The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling”

Frank Wood

The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling

John Paisley, Chong Wang, David Blei

Discussion of “A conditional game for comparing approximations”

Vincent Conitzer

A conditional game for comparing approximations

Frederik Eaton

Discussion of “Spectral Dimensionality Reduction via Maximum Entropy”

Laurens van der Maaten

Spectral Dimensionality Reduction via Maximum Entropy

Neil Lawrence

Discussion of “Learning Scale Free Networks by Reweighted ℓ_1 regularization”

Deepak Agarwal

Learning Scale Free Networks by Reweighted ℓ_1 regularization

Qiang Liu, Alexander Ihler