ICML 2006 Proceedings

^₀₀₁

^{_{Using Inaccurate Models in Reinforcement Learning}}

^{_{Pieter Abbeel, Morgan Quigley, Andrew Y. Ng}}

^₀₀₂

^{_{Algorithms for Portfolio Management Based on the Newton Method}}

^{_{Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire}}

^₀₀₃

^{_{Higher Order Learning with Graphs}}

^{_{Sameer Agarwal, Kristin Branson, Serge Belongie}}

^₀₀₄

^{_{Ranking on Graph Data}}

^{_{Shivani Agarwal}}

^₀₀₅

^{_{Robust Probabilistic Projections}}

^{_{Cédric Archambeau, Nicolas Delannay, Michel Verleysen}}

^₀₀₆

^{_{A DC-Programming Algorithm for Kernel Selection}}

^{_{Andreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil}}

^₀₀₇

^{_{Relational Temporal Difference Learning}}

^{_{Nima Asgharbeygi, David Stracuzzi, Pat Langley}}

^₀₀₈

^{_{A New Approach to Data Driven Clustering}}

^{_{Arik Azran, Zoubin Ghahramani}}

^₀₀₉

^{_{Agnostic Active Learning}}

^{_{Maria-Florina Balcan, Alina Beygelzimer, John Langford}}

^₀₁₀

^{_{On a Theory of Learning with Similarity Functions}}

^{_{Maria-Florina Balcan, Avrim Blum}}

^₀₁₁

^{_{On Bayesian Bounds}}

^{_{Arindam Banerjee}}

^₀₁₂

^{_{Convex Optimization Techniques for Fitting Sparse Gaussian Graphical Models}}

^{_{Onureena Banerjee, Laurent El Ghaoui, Alexandre d'Aspremont, Georges Natsoulis}}

^₀₁₃

^{_{Cover Trees for Nearest Neighbor}}

^{_{Alina Beygelzimer, Sham Kakade, John Langford}}

^₀₁₄

^{_{Graph Model Selection using Maximum Likelihood}}

^{_{Ivona Bezáková, Adam Kalai, Rahul Santhanam}}

^₀₁₅

^{_{Dynamic Topic Models}}

^{_{David M. Blei, John D. Lafferty}}

^₀₁₆

^{_{Predictive Search Distributions}}

^{_{Edwin V. Bonilla, Christopher K.I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F.P. O'Boyle}}

^₀₁₇

^{_{Learning Predictive State Representations using Non-Blind Policies}}

^{_{Michael Bowling, Peter McCracken, Michael James, James Neufeld, Dana Wilkinson}}

^₀₁₈

^{_{Efficient Co-Regularized Least Squares Regression}}

^{_{Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel}}

^₀₁₉

^{_{Semi-Supervised Learning for Structured Output Variables}}

^{_{Ulf Brefeld, Tobias Scheffer}}

^₀₂₀

^{_{Fast Nonparametric Clustering with Gaussian Blurring Mean-Shift}}

^{_{Miguel Á. Carreira-Perpiñán}}

^₀₂₁

^{_{An Empirical Comparison of Supervised Learning Algorithms}}

^{_{Rich Caruana, Alexandru Niculescu-Mizil}}

^₀₂₂

^{_{Robust Euclidean Embedding}}

^{_{Lawrence Cayton, Sanjoy Dasgupta}}

^₀₂₃

^{_{Hierarchical Classification: Combining Bayes with SVM}}

^{_{Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni}}

^₀₂₄

^{_{A Continuation Method for Semi-Supervised SVMs}}

^{_{Olivier Chapelle, Mingmin Chi, Alexander Zien}}

^₀₂₅

^{_{A Regularization Framework for Multiple-Instance Learning}}

^{_{Pak-Ming Cheung, James T. Kwok}}

^₀₂₆

^{_{Trading Convexity for Scalability}}

^{_{Ronan Collobert, Fabian Sinz, Jason Weston, Léon Bottou}}

^₀₂₇

^{_{Learning Algorithms for Online Principal-Agent Problems (and Selling Goods Online)}}

^{_{Vincent Conitzer, Nikesh Garera}}

^₀₂₈

^{_{Dealing with Non-Stationary Environments using Context Detection}}

^{_{Bruno C. da Silva, Eduardo W. Basso, Ana L. C. Bazzan, Paulo M. Engel}}

^₀₂₉

^{_{Locally Adaptive Classification Piloted by Uncertainty}}

^{_{Juan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwok}}

^₀₃₀

^{_{The Relationship Between Precision-Recall and ROC Curves}}

^{_{Jesse Davis, Mark Goadrich}}

^₀₃₁

^{_{Discriminative Cluster Analysis}}

^{_{Fernando De la Torre, Takeo Kanade}}

^₀₃₂

^{_{Collaborative Prediction using Ensembles of Maximum Margin Matrix Factorizations}}

^{_{Dennis DeCoste}}

^₀₃₃

^{_{Learning the Structure of Factored Markov Decision Processes in Reinforcement Learning Problems}}

^{_{Thomas Degris, Olivier Sigaud, Pierre-Henri Wuillemin}}

^₀₃₄

^{_{Efficient Learning of Naive Bayes Classifiers under Class-Conditional Classification Noise}}

^{_{Francois Denis, Christophe Nicolas Magnan, Liva Ralaivola}}

^₀₃₅

^{_{Learning User Preferences for Sets of Objects}}

^{_{Marie desJardins, Eric Eaton, Kiri L. Wagstaff}}

^₀₃₆

^{_{R1-PCA: Rotational Invariant L1-norm Principal Component Analysis for Robust Subspace Factorization}}

^{_{Chris Ding, Ding Zhou, Xiaofeng He, Hongyuan Zha}}

^₀₃₇

^{_{Clustering Documents with an Exponential-Family Approximation of the Dirichlet Compound Multinomial Distribution}}

^{_{Charles Elkan}}

^₀₃₈

^{_{A Graphical Model for Predicting Protein Molecular Function}}

^{_{Barbara Engelhardt, Michael Jordan, Steven Brenner}}

^₀₃₉

^{_{Qualitative Reinforcement Learning}}

^{_{Arkady Epshteyn, Gerald DeJong}}

^₀₄₀

^{_{Online Multiclass Learning by Interclass Hypothesis Sharing}}

^{_{Michael Fink, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman}}

^₀₄₁

^{_{Regression with the Optimised Combination Technique}}

^{_{Jochen Garcke}}

^₀₄₂

^{_{A Note on Mixtures of Experts for Multiclass Responses: Approximation Rate and Consistent Bayesian Inference}}

^{_{Yang Ge, Wenxin Jiang}}

^₀₄₃

^{_{The Rate Adapting Poisson Model for Information Retrieval and Object Recognition}}

^{_{Peter V. Gehler, Alex D. Holub, Max Welling}}

^₀₄₄

^{_{Kernelizing the Output of Tree-Based Methods}}

^{_{Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc}}

^₀₄₅

^{_{Nightmare at Test Time: Robust Learning by Feature Deletion}}

^{_{Amir Globerson, Sam Roweis}}

^₀₄₆

^{_{A Choice Model with Infinitely Many Latent Features}}

^{_{Dilan Görür, Frank Jäkel, Carl Edward Rasmussen}}

^₀₄₇

^{_{Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks}}

^{_{Alex Graves, Santiago Fernández, Faustino Gomez, Juergen Schmidhuber}}

^₀₄₈

^{_{Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering}}

^{_{Derek Greene, Pádraig Cunningham}}

^₀₄₉

^{_{Fast Transpose Methods for Kernel Learning on Sparse Data}}

^{_{Patrick Haffner}}

^₀₅₀

^{_{An Analysis of Graph Cut Size for Transductive Learning}}

^{_{Steve Hanneke}}

^₀₅₁

^{_{Learning a Kernel Function for Classification with Small Training Samples}}

^{_{Tomer Hertz, Aharon Bar Hillel, Daphna Weinshall}}

^₀₅₂

^{_{Looping Suffix Tree-Based Inference of Partially Observable Hidden State}}

^{_{Michael Holmes, Charles Lee Isbell, Jr.}}

^₀₅₃

^{_{Batch Mode Active Learning and Its Application to Medical Image Classification}}

^{_{Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu}}

^₀₅₄

^{_{Ranking Individuals by Group Comparisons}}

^{_{Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng}}

^₀₅₅

^{_{Hidden Process Models}}

^{_{Rebecca A. Hutchinson, Tom M. Mitchell, Indrayana Rustandi}}

^₀₅₆

^{_{Estimating Relatedness via Data Compression}}

^{_{Brendan Juba}}

^₀₅₇

^{_{Automatic Basis Function Construction for Approximate Dynamic Programming and Reinforcement Learning}}

^{_{Philipp W. Keller, Shie Mannor, Doina Precup}}

^₀₅₈

^{_{Personalized Handwriting Recognition via Biased Regularization}}

^{_{Wolf Kienzle, Kumar Chellapilla}}

^₀₅₉

^{_{Optimal Kernel Selection in Kernel Fisher Discriminant Analysis}}

^{_{Seung-Jean Kim, Alessandro Magnani, Stephen Boyd}}

^₀₆₀

^{_{Pareto Optimal Linear Classification}}

^{_{Seung-Jean Kim, Alessandro Magnani, Sikandar Samar, Stephen Boyd, Johan Lim}}

^₀₆₁

^{_{Fast Particle Smoothing: If I Had a Million Particles}}

^{_{Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang}}

^₀₆₂

^{_{Autonomous Shaping: Knowledge Transfer in Reinforcement Learning}}

^{_{George Konidaris, Andrew Barto}}

^₀₆₃

^{_{Data Association for Topic Intensity Tracking}}

^{_{Andreas Krause, Jure Leskovec, Carlos Guestrin}}

^₀₆₄

^{_{Learning Low-Rank Kernel Matrices}}

^{_{Brian Kulis, Mátyás Sustik, Inderjit Dhillon}}

^₀₆₅

^{_{Local Distance Preservation in the GP-LVM through Back Constraints}}

^{_{Neil D. Lawrence, Joaquin Quiñonero-Candela}}

^₀₆₆

^{_{Simpler Knowledge-based Support Vector Machines}}

^{_{Quoc V. Le, Alex J. Smola, Thomas Gärtner}}

^₀₆₇

^{_{Using Query-Specific Variance Estimates to Combine Bayesian Classifiers}}

^{_{Chi-Hoon Lee, Russ Greiner, Shaojun Wang}}

^₀₆₈

^{_{A Probabilistic Model for Text Kernels}}

^{_{Alain Lehmann, John Shawe-Taylor}}

^₀₆₉

^{_{Efficient MAP Approximation for Dense Energy Functions}}

^{_{Marius Leordeanu, Martial Hebert}}

^₀₇₀

^{_{Nonstationary Kernel Combination}}

^{_{Darrin P. Lewis, Tony Jebara, William Stafford Noble}}

^₀₇₁

^{_{Region-Based Value Iteration for Partially Observable Markov Decision Processes}}

^{_{Hui Li, Xuejun Liao, Lawrence Carin}}

^₀₇₂

^{_{Multiclass Boosting with Repartitioning}}

^{_{Ling Li}}

^₀₇₃

^{_{Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations}}

^{_{Wei Li, Andrew McCallum}}

^₀₇₄

^{_{Spectral Clustering for Multi-type Relational Data}}

^{_{Bo Long, Mark Zhang, Xiaoyun Wu, Philip S. Yu}}

^₀₇₅

^{_{Combined Central and Subspace Clustering for Computer Vision Applications}}

^{_{Le Lu, René Vidal}}

^₀₇₆

^{_{Fast Direct Policy Evaluation using Multiscale Analysis of Markov Diffusion Processes}}

^{_{Mauro Maggioni, Sridhar Mahadevan}}

^₀₇₇

^{_{Pruning in Ordered Bagging Ensembles}}

^{_{Gonzalo Martínez-Muñoz, Alberto Suárez}}

^₀₇₈

^{_{Learning High-Order MRF Priors of Color Images}}

^{_{Julian J. McAuley, Tibério S. Caetano, Alex J. Smola, Matthias O. Franz}}

^₀₇₉

^{_{The Uniqueness of a Good Optimum for K-means}}

^{_{Marina Meila}}

^₀₈₀

^{_{Kernel Information Embeddings}}

^{_{Roland Memisevic}}

^₀₈₁

^{_{Generalized Spectral Bounds for Sparse LDA}}

^{_{Baback Moghaddam, Yair Weiss, Shai Avidan}}

^₀₈₂

^{_{Learning to Impersonate}}

^{_{Moni Naor, Guy N. Rothblum}}

^₀₈₃

^{_{Online Decoding of Markov Models under Latency Constraints}}

^{_{Mukund Narasimhan, Paul Viola, Michael Shilman}}

^₀₈₄

^{_{Learning Hierarchical Task Networks by Observation}}

^{_{Negin Nejati, Pat Langley, Tolga Konik}}

^₀₈₅

^{_{Reinforcement Learning for Optimized Trade Execution}}

^{_{Yuriy Nevmyvaka, Yi Feng, Michael Kearns}}

^₀₈₆

^{_{Concept Boundary Detection for Speeding up SVMs}}

^{_{Navneet Panda, Edward Y. Chang, Gang Wu}}

^₀₈₇

^{_{The Support Vector Decomposition Machine}}

^{_{Francisco Pereira, Geoffrey Gordon}}

^₀₈₈

^{_{An Analytic Solution to Discrete Bayesian Reinforcement Learning}}

^{_{Pascal Poupart, Nikos Vlassis, Jesse Hoey, Kevin Regan}}

^₀₈₉

^{_{MISSL: Multiple-Instance Semi-Supervised Learning}}

^{_{Rouhollah Rahmani, Sally A. Goldman}}

^₀₉₀

^{_{Constructing Informative Priors using Transfer Learning}}

^{_{Rajat Raina, Andrew Y. Ng, Daphne Koller}}

^₀₉₁

^{_{Liva Ralaivola, François Denis, Christophe N. Magnan}}

^₀₉₂

^{_{Maximum Margin Planning}}

^{_{Nathan D. Ratliff, J. Andrew Bagnell, Martin A. Zinkevich}}

^₀₉₃

^{_{Quadratic Programming Relaxations for Metric Labeling and Markov Random Field MAP Estimation}}

^{_{Pradeep Ravikumar, John Lafferty}}

^₀₉₄

^{_{Categorization in Multiple Category Systems}}

^{_{Jean-Michel Renders, Eric Gaussier, Cyril Goutte, Francois Pacull, Gabriela Csurka}}

^₀₉₅

^{_{How Boosting the Margin Can Also Boost Classifier Complexity}}

^{_{Lev Reyzin, Robert E. Schapire}}

^₀₉₆

^{_{Combining Discriminative Features to Infer Complex Trajectories}}

^{_{David A. Ross, Simon Osindero, Richard S. Zemel}}

^₀₉₇

^{_{Sequential Update of ADtrees}}

^{_{Josep Roure, Andrew W. Moore}}

^₀₉₈

^{_{Predictive Linear-Gaussian Models of Controlled Stochastic Dynamical Systems}}

^{_{Matthew Rudary, Satinder Singh}}

^₀₉₉

^{_{A Statistical Approach to Rule Learning}}

^{_{Ulrich Rückert, Stefan Kramer}}

^₁₀₀

^{_{Efficient Inference on Sequence Segmentation Models}}

^{_{Sunita Sarawagi}}

^₁₀₁

^{_{Cost-Sensitive Learning with Conditional Markov Networks}}

^{_{Prithviraj Sen, Lise Getoor}}

^₁₀₂

^{_{Feature Value Acquisition in Testing: A Sequential Batch Test Algorithm}}

^{_{Victor S. Sheng, Charles X. Ling}}

^₁₀₃

^{_{Permutation Invariant SVMs}}

^{_{Pannagadatta K. Shivaswamy, Tony Jebara}}

^₁₀₄

^{_{Bayesian Learning of Measurement and Structural Models}}

^{_{Ricardo Silva, Richard Scheines}}

^₁₀₅

^{_{An Intrinsic Reward Mechanism for Efficient Exploration}}

^{_{Özgür Simsek, Andrew G. Barto}}

^₁₀₆

^{_{Deterministic Annealing for Semi-supervised Kernel Machines}}

^{_{Vikas Sindhwani, Sathiya Keerthi, Olivier Chapelle}}

^₁₀₇

^{_{Feature Subset Selection Bias for Classification Learning}}

^{_{Surendra K. Singhi, Huan Liu}}

^₁₀₈

^{_{Classifying EEG for Brain-Computer Interfaces: Learning Optimal Filters for Dynamical System Features}}

^{_{Le Song, Julien Epps}}

^₁₀₉

^{_{An Investigation of Computational and Informational Limits in Gaussian Mixture Clustering}}

^{_{Nathan Srebro, Gregory Shakhnarovich, Sam Roweis}}

^₁₁₀

^{_{Bayesian Pattern Ranking for Move Prediction in the Game of Go}}

^{_{David Stern, Ralf Herbrich, Thore Graepel}}

^₁₁₁

^{_{PAC Model-free Reinforcement Learning}}

^{_{Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman}}

^₁₁₂

^{_{Experience-Efficient Learning in Associative Bandit Problems}}

^{_{Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh}}

^₁₁₃

^{_{Full Bayesian Network Classifiers}}

^{_{Jiang Su, Harry Zhang}}

^₁₁₄

^{_{Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction}}

^{_{Masashi Sugiyama}}

^₁₁₅

^{_{Iterative RELIEF for Feature Weighting}}

^{_{Yijun Sun, Jian Li}}

^₁₁₆

^{_{Multiclass Reduced-Set Support Vector Machines}}

^{_{Benyang Tang, Dominic Mazzoni}}

^₁₁₇

^{_{Fast and Space Efficient String Kernels using Suffix Arrays}}

^{_{Choon Hui Teo, S.V.N. Vishwanathan}}

^₁₁₈

^{_{Bayesian Regression with Input Noise for High Dimensional Data}}

^{_{Jo-Anne Ting, Aaron D'Souza, Stefan Schaal}}

^₁₁₉

^{_{Probabilistic Inference for Solving Discrete and Continuous State Markov Decision Processes}}

^{_{Marc Toussaint, Amos Storkey}}

^₁₂₀

^{_{Clustering Graphs by Weighted Substructure Mining}}

^{_{Koji Tsuda, Taku Kudo}}

^₁₂₁

^{_{Active Sampling for Detecting Irrelevant Features}}

^{_{Sriharsha Veeramachaneni, Emanuele Olivetti, Paolo Avesani}}

^₁₂₂

^{_{Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods}}

^{_{S.V.N. Vishwanathan, Nicol N. Schraudolph, Mark W. Schmidt, Kevin P. Murphy}}

^₁₂₃

^{_{Topic Modeling: Beyond Bag-of-Words}}

^{_{Hanna M. Wallach}}

^₁₂₄

^{_{Label Propagation through Linear Neighborhoods}}

^{_{Fei Wang, Changshui Zhang}}

^₁₂₅

^{_{Two-Dimensional Solution Path for Support Vector Regression}}

^{_{Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky}}

^₁₂₆

^{_{Totally Corrective Boosting Algorithms that Maximize the Margin}}

^{_{Manfred K. Warmuth, Jun Liao, Gunnar Rätsch}}

^₁₂₇

^{_{Inference with the Universum}}

^{_{Jason Weston, Ronan Collobert, Fabian Sinz, Léon Bottou, Vladimir Vapnik}}

^₁₂₈

^{_{Kernel Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems}}

^{_{David Wingate, Satinder Singh}}

^₁₂₉

^{_{Predictive State Representations with Options}}

^{_{Britton Wolfe, Satinder Singh}}

^₁₃₀

^{_{Fast Time Series Classification using Numerosity Reduction}}

^{_{Xiaopeng Xi, Eamonn Keogh, Christian Shelton, Li Wei, Chotirat Ann Ratanamahatana}}

^₁₃₁

^{_{A Duality View of Spectral Methods for Dimensionality Reduction}}

^{_{Lin Xiao, Jun Sun, Stephen Boyd}}

^₁₃₂

^{_{Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture}}

^{_{Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee-Whye Teh}}

^₁₃₃

^{_{Discriminative Unsupervised Learning of Structured Predictors}}

^{_{Linli Xu, Dana Wilkinson, Finnegan Southey, Dale Schuurmans}}

^₁₃₄

^{_{Semi-Supervised Nonlinear Dimensionality Reduction}}

^{_{Xin Yang, Haoying Fu, Hongyuan Zha, Jesse Barlow}}

^₁₃₅

^{_{Null Space versus Orthogonal Linear Discriminant Analysis}}

^{_{Jieping Ye, Tao Xiong}}

^₁₃₆

^{_{Active Learning via Transductive Experimental Design}}

^{_{Kai Yu, Jinbo Bi, Volker Tresp}}

^₁₃₇

^{_{Collaborative Ordinal Regression}}

^{_{Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel}}

^₁₃₈

^{_{Block-Quantized Kernel Matrix for Fast Spectral Embedding}}

^{_{Kai Zhang, James Kwok}}

^₁₃₉

^{_{Statistical Debugging: Simultaneous Identification of Multiple Bugs}}

^{_{Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken}}

^₁₄₀

^{_{Efficient Lazy Elimination for Averaged One-Dependence Estimators}}

^{_{Fei Zheng, Geoffrey I. Webb}}