ML Anthology

ICML 2009

179 papers

A Bayesian Approach to Protein Model Quality Assessment Hetunandan Kamisetty, Christopher James Langmead
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A Convex Formulation for Learning Shared Structures from Multiple Tasks Jianhui Chen, Lei Tang, Jun Liu, Jieping Ye
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A Least Squares Formulation for a Class of Generalized Eigenvalue Problems in Machine Learning Liang Sun, Shuiwang Ji, Jieping Ye
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A Majorization-Minimization Algorithm for (multiple) Hyperparameter Learning Chuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng
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A Scalable Framework for Discovering Coherent Co-Clusters in Noisy Data Meghana Deodhar, Gunjan Gupta, Joydeep Ghosh, Hyuk Cho, Inderjit S. Dhillon
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A Simpler Unified Analysis of Budget Perceptrons Ilya Sutskever
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A Stochastic Memoizer for Sequence Data Frank D. Wood, Cédric Archambeau, Jan Gasthaus, Lancelot James, Yee Whye Teh
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ABC-Boost: Adaptive Base Class Boost for Multi-Class Classification Ping Li
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Accelerated Sampling for the Indian Buffet Process Finale Doshi-Velez, Zoubin Ghahramani
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Accounting for Burstiness in Topic Models Gabriel Doyle, Charles Elkan
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Active Learning for Directed Exploration of Complex Systems Michael C. Burl, Esther Wang
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An Accelerated Gradient Method for Trace Norm Minimization Shuiwang Ji, Jieping Ye
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An Efficient Projection for L1,infinity Regularization Ariadna Quattoni, Xavier Carreras, Michael Collins, Trevor Darrell
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An Efficient Sparse Metric Learning in High-Dimensional Space via L1-Penalized Log-Determinant Regularization Guo-Jun Qi, Jinhui Tang, Zheng-Jun Zha, Tat-Seng Chua, Hong-Jiang Zhang
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Analytic Moment-Based Gaussian Process Filtering Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hanebeck
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Approximate Inference for Planning in Stochastic Relational Worlds Tobias Lang, Marc Toussaint
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Archipelago: Nonparametric Bayesian Semi-Supervised Learning Ryan Prescott Adams, Zoubin Ghahramani
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Bandit-Based Optimization on Graphs with Application to Library Performance Tuning Frédéric de Mesmay, Arpad Rimmel, Yevgen Voronenko, Markus Püschel
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Bayesian Clustering for Email Campaign Detection Peter Haider, Tobias Scheffer
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Bayesian Inference for Plackett-Luce Ranking Models John Guiver, Edward Lloyd Snelson
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Binary Action Search for Learning Continuous-Action Control Policies Jason Pazis, Michail G. Lagoudakis
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Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties Ondrej Kuzelka, Filip Zelezný
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Blockwise Coordinate Descent Procedures for the Multi-Task Lasso, with Applications to Neural Semantic Basis Discovery Han Liu, Mark Palatucci, Jian Zhang
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BoltzRank: Learning to Maximize Expected Ranking Gain Maksims Volkovs, Richard S. Zemel
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Boosting Products of Base Classifiers Balázs Kégl, Róbert Busa-Fekete
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Boosting with Structural Sparsity John C. Duchi, Yoram Singer
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Compositional Noisy-Logical Learning Alan L. Yuille, Songfeng Zheng
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Constraint Relaxation in Approximate Linear Programs Marek Petrik, Shlomo Zilberstein
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Convex Variational Bayesian Inference for Large Scale Generalized Linear Models Hannes Nickisch, Matthias W. Seeger
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Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations Honglak Lee, Roger B. Grosse, Rajesh Ranganath, Andrew Y. Ng
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Curriculum Learning Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston
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Decision Tree and Instance-Based Learning for Label Ranking Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier
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Deep Learning from Temporal Coherence in Video Hossein Mobahi, Ronan Collobert, Jason Weston
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Deep Transfer via Second-Order Markov Logic Jesse Davis, Pedro M. Domingos
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Detecting the Direction of Causal Time Series Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
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Discovering Options from Example Trajectories Peng Zang, Peng Zhou, David Minnen, Charles Lee Isbell Jr.
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Discriminative K-Metrics Arthur Szlam, Guillermo Sapiro
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Domain Adaptation from Multiple Sources via Auxiliary Classifiers Lixin Duan, Ivor W. Tsang, Dong Xu, Tat-Seng Chua
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Dynamic Analysis of Multiagent Q-Learning with Ε-Greedy Exploration Eduardo Rodrigues Gomes, Ryszard Kowalczyk
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Dynamic Mixed Membership Blockmodel for Evolving Networks Wenjie Fu, Le Song, Eric P. Xing
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Efficient Euclidean Projections in Linear Time Jun Liu, Jieping Ye
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Efficient Learning Algorithms for Changing Environments Elad Hazan, C. Seshadhri
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EigenTransfer: A Unified Framework for Transfer Learning Wenyuan Dai, Ou Jin, Gui-Rong Xue, Qiang Yang, Yong Yu
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Evaluation Methods for Topic Models Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov, David M. Mimno
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Exploiting Sparse Markov and Covariance Structure in Multiresolution Models Myung Jin Choi, Venkat Chandrasekaran, Alan S. Willsky
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Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style Graham W. Taylor, Geoffrey E. Hinton
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Fast Evolutionary Maximum Margin Clustering Fabian Gieseke, Tapio Pahikkala, Oliver Kramer
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Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora
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Feature Hashing for Large Scale Multitask Learning Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg
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Fitting a Graph to Vector Data Samuel I. Daitch, Jonathan A. Kelner, Daniel A. Spielman
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Function Factorization Using Warped Gaussian Processes Mikkel N. Schmidt
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GAODE and HAODE: Two Proposals Based on AODE to Deal with Continuous Variables M. Julia Flores, José A. Gámez, Ana M. Martínez, José M. Puerta
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Generalization Analysis of Listwise Learning-to-Rank Algorithms Yanyan Lan, Tie-Yan Liu, Zhiming Ma, Hang Li
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Geometry-Aware Metric Learning Zhengdong Lu, Prateek Jain, Inderjit S. Dhillon
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Good Learners for Evil Teachers Ofer Dekel, Ohad Shamir
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Gradient Descent with Sparsification: An Iterative Algorithm for Sparse Recovery with Restricted Isometry Property Rahul Garg, Rohit Khandekar
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Grammatical Inference as a Principal Component Analysis Problem Raphaël Bailly, François Denis, Liva Ralaivola
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Graph Construction and B-Matching for Semi-Supervised Learning Tony Jebara, Jun Wang, Shih-Fu Chang
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Group Lasso with Overlap and Graph Lasso Laurent Jacob, Guillaume Obozinski, Jean-Philippe Vert
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Herding Dynamical Weights to Learn Max Welling
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Hilbert Space Embeddings of Conditional Distributions with Applications to Dynamical Systems Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu
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Hoeffding and Bernstein Races for Selecting Policies in Evolutionary Direct Policy Search Verena Heidrich-Meisner, Christian Igel
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Identifying Suspicious URLs: An Application of Large-Scale Online Learning Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker
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Importance Weighted Active Learning Alina Beygelzimer, Sanjoy Dasgupta, John Langford
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Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors David Andrzejewski, Xiaojin Zhu, Mark Craven
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Independent Factor Topic Models Duangmanee Putthividhya, Hagai Thomas Attias, Srikantan S. Nagarajan
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Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary? Xuan Vinh Nguyen, Julien Epps, James Bailey
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Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem Yisong Yue, Thorsten Joachims
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Invited Talk: Can Learning Kernels Help Performance? Corinna Cortes
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Invited Talk: Drifting Games, Boosting and Online Learning Yoav Freund
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K-Means in Space: A Radiation Sensitivity Evaluation Kiri L. Wagstaff, Benjamin J. Bornstein
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Kernelized Value Function Approximation for Reinforcement Learning Gavin Taylor, Ronald Parr
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Large Margin Training for Hidden Markov Models with Partially Observed States Trinh Minh Tri Do, Thierry Artières
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Large-Scale Collaborative Prediction Using a Nonparametric Random Effects Model Kai Yu, John D. Lafferty, Shenghuo Zhu, Yihong Gong
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Large-Scale Deep Unsupervised Learning Using Graphics Processors Rajat Raina, Anand Madhavan, Andrew Y. Ng
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Learning Complex Motions by Sequencing Simpler Motion Templates Gerhard Neumann, Wolfgang Maass, Jan Peters
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Learning Dictionaries of Stable Autoregressive Models for Audio Scene Analysis Youngmin Cho, Lawrence K. Saul
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Learning from Measurements in Exponential Families Percy Liang, Michael I. Jordan, Dan Klein
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Learning Instance Specific Distances Using Metric Propagation De-Chuan Zhan, Ming Li, Yufeng Li, Zhi-Hua Zhou
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Learning Kernels from Indefinite Similarities Yihua Chen, Maya R. Gupta, Benjamin Recht
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Learning Linear Dynamical Systems Without Sequence Information Tzu-Kuo Huang, Jeff G. Schneider
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Learning Markov Logic Network Structure via Hypergraph Lifting Stanley Kok, Pedro M. Domingos
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Learning Non-Redundant Codebooks for Classifying Complex Objects Wei Zhang, Akshat Surve, Xiaoli Z. Fern, Thomas G. Dietterich
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Learning Nonlinear Dynamic Models John Langford, Ruslan Salakhutdinov, Tong Zhang
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Learning Prediction Suffix Trees with Winnow Nikolaos Karampatziakis, Dexter Kozen
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Learning Spectral Graph Transformations for Link Prediction Jérôme Kunegis, Andreas Lommatzsch
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Learning Structural SVMs with Latent Variables Chun-Nam John Yu, Thorsten Joachims
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Learning Structurally Consistent Undirected Probabilistic Graphical Models Sushmita Roy, Terran Lane, Margaret Werner-Washburne
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Learning to Segment from a Few Well-Selected Training Images Alireza Farhangfar, Russell Greiner, Csaba Szepesvári
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Learning When to Stop Thinking and Do Something! Barnabás Póczos, Yasin Abbasi-Yadkori, Csaba Szepesvári, Russell Greiner, Nathan R. Sturtevant
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Learning with Structured Sparsity Junzhou Huang, Tong Zhang, Dimitris N. Metaxas
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Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul
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MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification Jun Zhu, Amr Ahmed, Eric P. Xing
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Model-Free Reinforcement Learning as Mixture Learning Nikos Vlassis, Marc Toussaint
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Monte-Carlo Simulation Balancing David Silver, Gerald Tesauro
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More Generality in Efficient Multiple Kernel Learning Manik Varma, Bodla Rakesh Babu
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Multi-Assignment Clustering for Boolean Data Andreas P. Streich, Mario Frank, David A. Basin, Joachim M. Buhmann
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Multi-Class Image Segmentation Using Conditional Random Fields and Global Classification Nils Plath, Marc Toussaint, Shinichi Nakajima
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Multi-Instance Learning by Treating Instances as Non-I.I.D. Samples Zhi-Hua Zhou, Yu-Yin Sun, Yufeng Li
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Multi-View Clustering via Canonical Correlation Analysis Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu, Karthik Sridharan
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Multiple Indefinite Kernel Learning with Mixed Norm Regularization Matthieu Kowalski, Marie Szafranski, Liva Ralaivola
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Near-Bayesian Exploration in Polynomial Time J. Zico Kolter, Andrew Y. Ng
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Nearest Neighbors in High-Dimensional Data: The Emergence and Influence of Hubs Milos Radovanovic, Alexandros Nanopoulos, Mirjana Ivanovic
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Non-Linear Matrix Factorization with Gaussian Processes Neil D. Lawrence, Raquel Urtasun
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Non-Monotonic Feature Selection Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, Irwin King
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Nonparametric Estimation of the Precision-Recall Curve Stéphan Clémençon, Nicolas Vayatis
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Nonparametric Factor Analysis with Beta Process Priors John W. Paisley, Lawrence Carin
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On Primal and Dual Sparsity of Markov Networks Jun Zhu, Eric P. Xing
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On Sampling-Based Approximate Spectral Decomposition Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
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Online Dictionary Learning for Sparse Coding Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro
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Online Feature Elicitation in Interactive Optimization Craig Boutilier, Kevin Regan, Paolo Viappiani
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Online Learning by Ellipsoid Method Liu Yang, Rong Jin, Jieping Ye
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Optimal Reverse Prediction: A Unified Perspective on Supervised, Unsupervised and Semi-Supervised Learning Linli Xu, Martha White, Dale Schuurmans
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Optimistic Initialization and Greediness Lead to Polynomial Time Learning in Factored MDPs Istvan Szita, András Lörincz
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Optimized Expected Information Gain for Nonlinear Dynamical Systems Alberto Giovanni Busetto, Cheng Soon Ong, Joachim M. Buhmann
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Orbit-Product Representation and Correction of Gaussian Belief Propagation Jason K. Johnson, Vladimir Y. Chernyak, Michael Chertkov
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PAC-Bayesian Learning of Linear Classifiers Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
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Partial Order Embedding with Multiple Kernels Brian McFee, Gert R. G. Lanckriet
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Partially Supervised Feature Selection with Regularized Linear Models Thibault Helleputte, Pierre Dupont
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Piecewise-Stationary Bandit Problems with Side Observations Jia Yuan Yu, Shie Mannor
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Polyhedral Outer Approximations with Application to Natural Language Parsing André F. T. Martins, Noah A. Smith, Eric P. Xing
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Predictive Representations for Policy Gradient in POMDPs Abdeslam Boularias, Brahim Chaib-draa
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Probabilistic Dyadic Data Analysis with Local and Global Consistency Deng Cai, Xuanhui Wang, Xiaofei He
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Proto-Predictive Representation of States with Simple Recurrent Temporal-Difference Networks Takaki Makino
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Prototype Vector Machine for Large Scale Semi-Supervised Learning Kai Zhang, James T. Kwok, Bahram Parvin
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Proximal Regularization for Online and Batch Learning Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo
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Ranking Interesting Subgroups Stefan Rüping
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Ranking with Ordered Weighted Pairwise Classification Nicolas Usunier, David Buffoni, Patrick Gallinari
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Regression by Dependence Minimization and Its Application to Causal Inference in Additive Noise Models Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf
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Regularization and Feature Selection in Least-Squares Temporal Difference Learning J. Zico Kolter, Andrew Y. Ng
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Robot Trajectory Optimization Using Approximate Inference Marc Toussaint
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Robust Bounds for Classification via Selective Sampling Nicolò Cesa-Bianchi, Claudio Gentile, Francesco Orabona
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Robust Feature Extraction via Information Theoretic Learning Xiaotong Yuan, Bao-Gang Hu
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Route Kernels for Trees Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti
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Rule Learning with Monotonicity Constraints Wojciech Kotlowski, Roman Slowinski
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Semi-Supervised Learning Using Label Mean Yufeng Li, James T. Kwok, Zhi-Hua Zhou
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Sequential Bayesian Prediction in the Presence of Changepoints Roman Garnett, Michael A. Osborne, Stephen J. Roberts
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SimpleNPKL: Simple Non-Parametric Kernel Learning Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
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Solution Stability in Linear Programming Relaxations: Graph Partitioning and Unsupervised Learning Sebastian Nowozin, Stefanie Jegelka
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Sparse Gaussian Graphical Models with Unknown Block Structure Benjamin M. Marlin, Kevin P. Murphy
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Sparse Higher Order Conditional Random Fields for Improved Sequence Labeling Xian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huang, Lide Wu
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Spectral Clustering Based on the Graph P-Laplacian Thomas Bühler, Matthias Hein
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Split Variational Inference Guillaume Bouchard, Onno Zoeter
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Stochastic Methods for L1 Regularized Loss Minimization Shai Shalev-Shwartz, Ambuj Tewari
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Stochastic Search Using the Natural Gradient Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
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Structure Learning of Bayesian Networks Using Constraints Cassio P. de Campos, Zhi Zeng, Qiang Ji
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Structure Learning with Independent Non-Identically Distributed Data Robert E. Tillman
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Structure Preserving Embedding Blake Shaw, Tony Jebara
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Supervised Learning from Multiple Experts: Whom to Trust When Everyone Lies a Bit Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Anna K. Jerebko, Charles Florin, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy
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Surrogate Regret Bounds for Proper Losses Mark D. Reid, Robert C. Williamson
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The Adaptive K-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning Carlos Diuk, Lihong Li, Bethany R. Leffler
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The Bayesian Group-Lasso for Analyzing Contingency Tables Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Volker Roth
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The Graphlet Spectrum Risi Kondor, Nino Shervashidze, Karsten M. Borgwardt
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Topic-Link LDA: Joint Models of Topic and Author Community Yan Liu, Alexandru Niculescu-Mizil, Wojciech Gryc
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Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities Ryan Prescott Adams, Iain Murray, David J. C. MacKay
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Trajectory Prediction: Learning to mAP Situations to Robot Trajectories Nikolay Jetchev, Marc Toussaint
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Transfer Learning for Collaborative Filtering via a Rating-Matrix Generative Model Bin Li, Qiang Yang, Xiangyang Xue
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Tutorial Summary: Active Learning Sanjoy Dasgupta, John Langford
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Tutorial Summary: Convergence of Natural Dynamics to Equilibria Eyal Even-Dar, Vahab S. Mirrokni
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Tutorial Summary: Large Social and Information Networks: Opportunities for ML Jure Leskovec
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Tutorial Summary: Learning with Dependencies Between Several Response Variables Volker Tresp, Kai Yu
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Tutorial Summary: Machine Learning in IR: Recent Successes and New Opportunities Paul N. Bennett, Misha Bilenko, Kevyn Collins-Thompson
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Tutorial Summary: Reductions in Machine Learning Alina Beygelzimer, John Langford, Bianca Zadrozny
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Tutorial Summary: Structured Prediction for Natural Language Processing Noah A. Smith
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Tutorial Summary: Survey of Boosting from an Optimization Perspective Manfred K. Warmuth, S. V. N. Vishwanathan
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Tutorial Summary: The Neuroscience of Reinforcement Learning Yael Niv
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Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision Vikas Sindhwani, Prem Melville, Richard D. Lawrence
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Unsupervised Hierarchical Modeling of Locomotion Styles Wei Pan, Lorenzo Torresani
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Unsupervised Search-Based Structured Prediction Hal Daumé Iii
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Using Fast Weights to Improve Persistent Contrastive Divergence Tijmen Tieleman, Geoffrey E. Hinton
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Workshop Summary: Abstraction in Reinforcement Learning Özgür Simsek
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Workshop Summary: Automated Interpretation and Modelling of Cell Images Robert F. Murphy, Chun-Nan Hsu, Loris Nanni
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Workshop Summary: Numerical Mathematics in Machine Learning Matthias W. Seeger, Suvrit Sra, John P. Cunningham
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Workshop Summary: On-Line Learning with Limited Feedback Jean-Yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko, Csaba Szepesvári
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Workshop Summary: Results of the 2009 Reinforcement Learning Competition David Wingate, Carlos Diuk, Lihong Li, Matthew Taylor, Jordan Frank
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Workshop Summary: Seventh Annual Workshop on Bayes Applications John Mark Agosta, Russell G. Almond, Dennis M. Buede, Marek J. Druzdzel, Judy Goldsmith, Silja Renooij
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Workshop Summary: Sparse Methods for Music Audio Douglas Eck, Dan Ellis, Philippe Hamel
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Workshop Summary: The Fourth Workshop on Evaluation Methods for Machine Learning Chris Drummond, Nathalie Japkowicz, William Klement, Sofus A. Macskassy
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Workshop Summary: Workshop on Learning Feature Hierarchies Kai Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio
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