ML Anthology

AISTATS 2011

106 papers

A Conditional Game for Comparing Approximations Frederik Eaton
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A Dynamic Relational Infinite Feature Model for Longitudinal Social Networks James Foulds, Christopher DuBois, Arthur Asuncion, Carter Butts, Padhraic Smyth
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A Fast Algorithm for Recovery of Jointly Sparse Vectors Based on the Alternating Direction Methods Hongtao Lu, Xianzhong Long, Jingyuan Lv
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A Finite Newton Algorithm for Non-Degenerate Piecewise Linear Systems Xiao–Tong Yuan, Shuicheng Yan
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A Novel Greedy Algorithm for Nyström Approximation Ahmed Farahat, Ali Ghodsi, Mohamed Kamel
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A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning Stephane Ross, Geoffrey Gordon, Drew Bagnell
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A Spike and Slab Restricted Boltzmann Machine Aaron Courville, James Bergstra, Yoshua Bengio
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Active Boosted Learning (ActBoost) Kirill Trapeznikov, Venkatesh Saligrama, David Castanon
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Active Clustering: Robust and Efficient Hierarchical Clustering Using Adaptively Selected Similarities Brian Eriksson, Gautam Dasarathy, Aarti Singh, Rob Nowak
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Active Diagnosis Under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach Gowtham Bellala, Suresh Bhavnani, Clayton Scott
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Adaptive Bandits: Towards the Best History-Dependent Strategy Maillard Odalric, Rémi Munos
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An Analysis of Single-Layer Networks in Unsupervised Feature Learning Adam Coates, Andrew Ng, Honglak Lee
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An Instantiation-Based Theorem Prover for First-Order Programming Erik Zawadzki, Geoffrey Gordon, Andre Platzer
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Approximate Inference for the Loss-Calibrated Bayesian Simon Lacoste–Julien, Ferenc Huszár, Zoubin Ghahramani
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Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening Ning Situ, Xiaojing Yuan, George Zouridakis
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Asymptotic Theory for Linear-Chain Conditional Random Fields Mathieu Sinn, Pascal Poupart
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Bagged Structure Learning of Bayesian Network Gal Elidan
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Bayesian Hierarchical Cross-Clustering Dazhuo Li, Patrick Shafto
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Block-Sparse Solutions Using Kernel Block RIP and Its Application to Group Lasso Rahul Garg, Rohit Khandekar
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Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality Shuang–Hong Yang, Steven P. Crain, Hongyuan Zha
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CAKE: Convex Adaptive Kernel Density Estimation Ravi Sastry Ganti Mahapatruni, Alexander Gray
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Can Matrix Coherence Be Efficiently and Accurately Estimated? Mehryar Mohri, Ameet Talwalkar
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Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models Edward Challis, David Barber
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Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection Bin Li, Steven C.H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan
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Contextual Bandit Algorithms with Supervised Learning Guarantees Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert Schapire
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Contextual Bandits with Linear Payoff Functions Wei Chu, Lihong Li, Lev Reyzin, Robert Schapire
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Convergent Decomposition Solvers for Tree-Reweighted Free Energies Jeremy Jancsary, Gerald Matz
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Convex Envelopes of Complexity Controlling Penalties: The Case Against Premature Envelopment Vladimir Jojic, Suchi Saria, Daphne Koller
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Cross-Domain Object Matching with Model Selection Makoto Yamada, Masashi Sugiyama
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Deep Learners Benefit More from Out-of-Distribution Examples Yoshua Bengio, Frédéric Bastien, Arnaud Bergeron, Nicolas Boulanger–Lewandowski, Thomas Breuel, Youssouf Chherawala, Moustapha Cisse, Myriam Côté, Dumitru Erhan, Jeremy Eustache, Xavier Glorot, Xavier Muller, Sylvain Pannetier Lebeuf, Razvan Pascanu, Salah Rifai, François Savard, Guillaume Sicard
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Deep Learning for Efficient Discriminative Parsing Ronan Collobert
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Deep Sparse Rectifier Neural Networks Xavier Glorot, Antoine Bordes, Yoshua Bengio
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Dependent Hierarchical Beta Process for Image Interpolation and Denoising Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro, David Dunson, Lawrence Carin
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Dimensionality Reduction for Spectral Clustering Donglin Niu, Jennifer Dy, Michael I. Jordan
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Directional Statistics on Permutations Sergey M. Plis, Stephen McCracken, Terran Lane, Vince D. Calhoun
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Discussion of “a Conditional Game for Comparing Approximations” Vincent Conitzer
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Discussion of “Contextual Bandit Algorithms with Supervised Learning Guarantees” Brendan McMahan
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Discussion of “Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables” Jiji Zhang, Ricardo Silva
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Discussion of “Learning Scale Free Networks by Reweighted $\ell_1$ Regularization” Deepak Agarwal
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Discussion of “Spectral Dimensionality Reduction via Maximum Entropy” Laurens Maaten
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Discussion of “The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling” Frank Wood
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Discussion of “The Neural Autoregressive Distribution Estimator” Yoshua Bengio
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Domain Adaptation with Coupled Subspaces John Blitzer, Sham Kakade, Dean Foster
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Dynamic Policy Programming with Function Approximation Mohammad Gheshlaghi Azar, Vicenç Gómez, Bert Kappen
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Efficient Variable Selection in Support Vector Machines via the Alternating Direction Method of Multipliers Gui–Bo Ye, Yifei Chen, Xiaohui Xie
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Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure Veselin Stoyanov, Alexander Ropson, Jason Eisner
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Error Analysis of Laplacian Eigenmaps for Semi-Supervised Learning Xueyuan Zhou, Nathan Srebro
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Estimating Beta-Mixing Coefficients Daniel McDonald, Cosma Shalizi, Mark Schervish
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Estimating Probabilities in Recommendation Systems Mingxuan Sun, Guy Lebanon, Paul Kidwell
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Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks Qirong Ho, Le Song, Eric Xing
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Faithfulness in Chain Graphs: The Gaussian Case Jose M. Peña
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Fast $b$-Matching via Sufficient Selection Belief Propagation Bert Huang, Tony Jebara
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Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference Matthias Seeger, Hannes Nickisch
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Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization Brendan McMahan
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Generalization Bound for Infinitely Divisible Empirical Process Chao Zhang, Dacheng Tao
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Generative Kernels for Exponential Families Arvind Agarwal, Hal Daumé
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Generative Modeling for Maximizing Precision and Recall in Information Visualization Jaakko Peltonen, Samuel Kaski
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Group Orthogonal Matching Pursuit for Logistic Regression Aurelie Lozano, Grzegorz Swirszcz, Naoki Abe
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Hidden-Unit Conditional Random Fields Laurens Maaten, Max Welling, Lawrence Saul
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Hierarchical Probabilistic Models for Group Anomaly Detection Liang Xiong, Barnabás Póczos, Jeff Schneider, Andrew Connolly, Jake VanderPlas
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Improved Loss Bounds for Multiple Kernel Learning Zakria Hussain, John Shawe–Taylor
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Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback Ankan Saha, Ambuj Tewari
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Information Theoretical Clustering via Semidefinite Programming Meihong Wang, Fei Sha
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Kernel Belief Propagation Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin
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Learning Class-Relevant Features and Class-Irrelevant Features via a Hybrid Third-Order RBM Heng Luo, Ruimin Shen, Changyong Niu, Carsten Ullrich
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Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables Robert Tillman, Peter Spirtes
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Learning from Positive and Unlabeled Examples by Enforcing Statistical Significance Pierre Geurts
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Learning Mixtures of Gaussians with Maximum-a-Posteriori Oracle Satyaki Mahalanabis
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Learning Scale Free Networks by Reweighted $\ell_1$ Regularization Qiang Liu, Alexander Ihler
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Lightweight Implementations of Probabilistic Programming Languages via Transformational Compilation David Wingate, Andreas Stuhlmueller, Noah Goodman
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Linear-Time Estimators for Propensity Scores Deepak Agarwal, Lihong Li, Alexander Smola
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Machine Learning Markets Amos Storkey
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Maximum Volume Clustering Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama
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Mixed Cumulative Distribution Networks Ricardo Silva, Charles Blundell, Yee Whye Teh
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Multi-Label Output Codes Using Canonical Correlation Analysis Yi Zhang, Jeff Schneider
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Multicore Gibbs Sampling in Dense, Unstructured Graphs Tianbing Xu, Alexander Ihler
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Multiscale Community Blockmodel for Network Exploration Qirong Ho, Ankur Parikh, Le Song, Eric Xing
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On Learning Discrete Graphical Models Using Group-Sparse Regularization Ali Jalali, Pradeep Ravikumar, Vishvas Vasuki, Sujay Sanghavi
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On NDCG Consistency of Listwise Ranking Methods Pradeep Ravikumar, Ambuj Tewari, Eunho Yang
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On the Estimation of $\alpha$-Divergences Barnabas Poczos, Jeff Schneider
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On Time Varying Undirected Graphs Mladen Kolar, Eric P. Xing
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Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text Amr Ahmed, Qirong Ho, Choon Hui Teo, Jacob Eisenstein, Alex Smola, Eric Xing
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Online Learning of Multiple Tasks and Their Relationships Avishek Saha, Piyush Rai, Hal Daumé Iii, Suresh Venkatasubramanian
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Online Learning of Structured Predictors with Multiple Kernels Andre Filipe Torres Martins, Noah Smith, Eric Xing, Pedro Aguiar, Mario Figueiredo
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Online Variational Inference for the Hierarchical Dirichlet Process Chong Wang, John Paisley, David M. Blei
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Optimal and Robust Price Experimentation: Learning by Lottery Christopher Dance, Onno Zoeter
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Optimal Distributed Market-Based Planning for Multi-Agent Systems with Shared Resources Sue Ann Hong, Geoffrey Gordon
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Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin
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Polytope Samplers for Inference in Ill-Posed Inverse Problems Edoardo Airoldi, Bertrand Haas
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Relational Learning with One Network: An Asymptotic Analysis Rongjing Xiang, Jennifer Neville
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Relative Entropy Inverse Reinforcement Learning Abdeslam Boularias, Jens Kober, Jan Peters
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Revisiting MAP Estimation, Message Passing and Perfect Graphs James Foulds, Nicholas Navaroli, Padhraic Smyth, Alexander Ihler
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Robust Bayesian Matrix Factorisation Balaji Lakshminarayanan, Guillaume Bouchard, Cedric Archambeau
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Semi-Supervised Learning by Higher Order Regularization Xueyuan Zhou, Mikhail Belkin
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Spectral Chinese Restaurant Processes: Nonparametric Clustering Based on Similarities Richard Socher, Andrew Maas, Christopher Manning
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Spectral Clustering on a Budget Ohad Shamir, Naftali Tishby
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Spectral Dimensionality Reduction via Maximum Entropy Neil Lawrence
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Statistical Optimization of Non-Negative Matrix Factorization Anoop Korattikara Balan, Levi Boyles, Max Welling, Jingu Kim, Haesun Park
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Switch-Reset Models : Exact and Approximate Inference Chris Bracegirdle, David Barber
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The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling John Paisley, Chong Wang, David Blei
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The Neural Autoregressive Distribution Estimator Hugo Larochelle, Iain Murray
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The Sample Complexity of Self-Verifying Bayesian Active Learning Liu Yang, Steve Hanneke, Jaime Carbonell
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Tighter Relaxations for MAP-MRF Inference: A Local Primal-Dual Gap Based Separation Algorithm Dhruv Batra, Sebastian Nowozin, Pushmeet Kohli
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TopicFlow Model: Unsupervised Learning of Topic-Specific Influences of Hyperlinked Documents Ramesh Nallapati, Daniel McFarland, Christopher Manning
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Two-Layer Multiple Kernel Learning Jinfeng Zhuang, Ivor W. Tsang, Steven C.H. Hoi
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Unsupervised Supervised Learning II: Margin-Based Classification Without Labels Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon
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