22. ICML 2005: Bonn, Germany

• Pieter Abbeel, Andrew Y. Ng:
  Exploration and apprenticeship learning in reinforcement learning. 1-8
  
• Brigham Anderson, Andrew Moore:
  Active learning for Hidden Markov Models: objective functions and algorithms. 9-16
  
• Nicos Angelopoulos, James Cussens:
  Tempering for Bayesian C&RT. 17-24
  
• Fabrizio Angiulli:
  Fast condensed nearest neighbor rule. 25-32
  
• Francis R. Bach, Michael I. Jordan:
  Predictive low-rank decomposition for kernel methods. 33-40
  
• Ron Bekkerman, Ran El-Yaniv, Andrew McCallum:
  Multi-way distributional clustering via pairwise interactions. 41-48
  
• Alina Beygelzimer, Varsha Dani, Thomas P. Hayes, John Langford, Bianca Zadrozny:
  Error limiting reductions between classification tasks. 49-56
  
• Hendrik Blockeel, David Page, Ashwin Srinivasan:
  Multi-instance tree learning. 57-64
  
• Michael H. Bowling, Ali Ghodsi, Dana F. Wilkinson:
  Action respecting embedding. 65-72
  
• Markus Breitenbach, Gregory Z. Grudic:
  Clustering through ranking on manifolds. 73-80
  
• Will Bridewell, Narges Bani Asadi, Pat Langley, Ljupco Todorovski:
  Reducing overfitting in process model induction. 81-88
  
• Christopher J. C. Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Gregory N. Hullender:
  Learning to rank using gradient descent. 89-96
  
• John Burge, Terran Lane:
  Learning class-discriminative dynamic Bayesian networks. 97-104
  
• Sylvain Calinon, Aude Billard:
  Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM. 105-112
  
• Michael Carney, Padraig Cunningham, Jim Dowling, Ciaran Lee:
  Predicting probability distributions for surf height using an ensemble of mixture density networks. 113-120
  
• Yu-Han Chang, Leslie Pack Kaelbling:
  Hedged learning: regret-minimization with learning experts. 121-128
  
• Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang:
  Variational Bayesian image modelling. 129-136
  
• Wei Chu, Zoubin Ghahramani:
  Preference learning with Gaussian processes. 137-144
  
• Wei Chu, S. Sathiya Keerthi:
  New approaches to support vector ordinal regression. 145-152
  
• Corinna Cortes, Mehryar Mohri, Jason Weston:
  A general regression technique for learning transductions. 153-160
  
• Jacob W. Crandall, Michael A. Goodrich:
  Learning to compete, compromise, and cooperate in repeated general-sum games. 161-168
  
• Hal Daumé III, Daniel Marcu:
  Learning as search optimization: approximate large margin methods for structured prediction. 169-176
  
• Fernando De la Torre, Takeo Kanade:
  Multimodal oriented discriminant analysis. 177-184
  
• Adam Drake, Dan Ventura:
  A practical generalization of Fourier-based learning. 185-192
  
• Kurt Driessens, Saso Dzeroski:
  Combining model-based and instance-based learning for first order regression. 193-200
  
• Yaakov Engel, Shie Mannor, Ron Meir:
  Reinforcement learning with Gaussian processes. 201-208
  
• Roberto Esposito, Lorenza Saitta:
  Experimental comparison between bagging and Monte Carlo ensemble classification. 209-216
  
• Thomas Finley, Thorsten Joachims:
  Supervised clustering with support vector machines. 217-224
  
• Holger Fröhlich, Jörg K. Wegner, Florian Sieker, Andreas Zell:
  Optimal assignment kernels for attributed molecular graphs. 225-232
  
• Pierre Geurts, Louis Wehenkel:
  Closed-form dual perturb and combine for tree-based models. 233-240
  
• Mark Girolami, Simon Rogers:
  Hierarchic Bayesian models for kernel learning. 241-248
  
• Karen A. Glocer, Damian Eads, James Theiler:
  Online feature selection for pixel classification. 249-256
  
• Eugene Grois, David C. Wilkins:
  Learning strategies for story comprehension: a reinforcement learning approach. 257-264
  
• Carlos Guestrin, Andreas Krause, Ajit Paul Singh:
  Near-optimal sensor placements in Gaussian processes. 265-272
  
• Gunjan Gupta, Joydeep Ghosh:
  Robust one-class clustering using hybrid global and local search. 273-280
  
• Xiaofei He, Deng Cai, Wanli Min:
  Statistical and computational analysis of locality preserving projection. 281-288
  
• Matthias Hein, Jean-Yves Audibert:
  Intrinsic dimensionality estimation of submanifolds in R^d. 289-296
  
• Katherine A. Heller, Zoubin Ghahramani:
  Bayesian hierarchical clustering. 297-304
  
• Mark Herbster, Massimiliano Pontil, Lisa Wainer:
  Online learning over graphs. 305-312
  
• Simon I. Hill, Arnaud Doucet:
  Adapting two-class support vector classification methods to many class problems. 313-320
  
• Shen-Shyang Ho:
  A martingale framework for concept change detection in time-varying data streams. 321-327
  
• Eugene Ie, Jason Weston, William Stafford Noble, Christina S. Leslie:
  Multi-class protein fold recognition using adaptive codes. 329-336
  
• Okhtay Ilghami, Héctor Muñoz-Avila, Dana S. Nau, David W. Aha:
  Learning approximate preconditions for methods in hierarchical plans. 337-344
  
• Neil Ireson, Fabio Ciravegna, Mary Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli:
  Evaluating machine learning for information extraction. 345-352
  
• Rong Jin, Joyce Y. Chai, Luo Si:
  Learn to weight terms in information retrieval using category information. 353-360
  
• Rong Jin, Jian Zhang:
  A smoothed boosting algorithm using probabilistic output codes. 361-368
  
• Yushi Jing, Vladimir Pavlovic, James M. Rehg:
  Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes. 369-376
  
• Thorsten Joachims:
  A support vector method for multivariate performance measures. 377-384
  
• Thorsten Joachims, John E. Hopcroft:
  Error bounds for correlation clustering. 385-392
  
• Sébastien Jodogne, Justus H. Piater:
  Interactive learning of mappings from visual percepts to actions. 393-400
  
• Anders Jonsson, Andrew G. Barto:
  A causal approach to hierarchical decomposition of factored MDPs. 401-408
  
• Matti Kääriäinen, John Langford:
  A comparison of tight generalization error bounds. 409-416
  
• S. Sathiya Keerthi:
  Generalized LARS as an effective feature selection tool for text classification with SVMs. 417-424
  
• Rinat Khoussainov, Andreas Heß, Nicholas Kushmerick:
  Ensembles of biased classifiers. 425-432
  
• Mikko Koivisto, Kismat Sood:
  Computational aspects of Bayesian partition models. 433-440
  
• Stanley Kok, Pedro Domingos:
  Learning the structure of Markov logic networks. 441-448
  
• Jeremy Z. Kolter, Marcus A. Maloof:
  Using additive expert ensembles to cope with concept drift. 449-456
  
• Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney:
  Semi-supervised graph clustering: a kernel approach. 457-464
  
• Thomas Navin Lal, Michael Schröder, N. Jeremy Hill, Hubert Preißl, Thilo Hinterberger, Jürgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas Hofmann, Niels Birbaumer, Bernhard Schölkopf:
  A brain computer interface with online feedback based on magnetoencephalography. 465-472
  
• John Langford, Bianca Zadrozny:
  Relating reinforcement learning performance to classification performance. 473-480
  
• François Laviolette, Mario Marchand:
  PAC-Bayes risk bounds for sample-compressed Gibbs classifiers. 481-488
  
• Quoc V. Le, Alexander J. Smola, Stéphane Canu:
  Heteroscedastic Gaussian process regression. 489-496
  
• Rui Leite, Pavel Brazdil:
  Predicting relative performance of classifiers from samples. 497-503
  
• Xuejun Liao, Ya Xue, Lawrence Carin:
  Logistic regression with an auxiliary data source. 505-512
  
• Yan Liu, Eric P. Xing, Jaime G. Carbonell:
  Predicting protein folds with structural repeats using a chain graph model. 513-520
  
• Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio:
  Unsupervised evidence integration. 521-528
  
• Daniel Lowd, Pedro Domingos:
  Naive Bayes models for probability estimation. 529-536
  
• Sofus A. Macskassy, Foster J. Provost, Saharon Rosset:
  ROC confidence bands: an empirical evaluation. 537-544
  
• Rasmus Elsborg Madsen, David Kauchak, Charles Elkan:
  Modeling word burstiness using the Dirichlet distribution. 545-552
  
• Sridhar Mahadevan:
  Proto-value functions: developmental reinforcement learning. 553-560
  
• Shie Mannor, Dori Peleg, Reuven Y. Rubinstein:
  The cross entropy method for classification. 561-568
  
• H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon:
  Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees. 569-576
  
• Marina Meila:
  Comparing clusterings: an axiomatic view. 577-584
  
• Sauro Menchetti, Fabrizio Costa, Paolo Frasconi:
  Weighted decomposition kernels. 585-592
  
• Jeff Michels, Ashutosh Saxena, Andrew Y. Ng:
  High speed obstacle avoidance using monocular vision and reinforcement learning. 593-600
  
• Sriraam Natarajan, Prasad Tadepalli:
  Dynamic preferences in multi-criteria reinforcement learning. 601-608
  
• Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar:
  Learning first-order probabilistic models with combining rules. 609-616
  
• DucDung Nguyen, Tu Bao Ho:
  An efficient method for simplifying support vector machines. 617-624
  
• Alexandru Niculescu-Mizil, Rich Caruana:
  Predicting good probabilities with supervised learning. 625-632
  
• Santiago Ontañón, Enric Plaza:
  Recycling data for multi-agent learning. 633-640
  
• Jean-François Paiement, Douglas Eck, Samy Bengio, David Barber:
  A graphical model for chord progressions embedded in a psychoacoustic space. 641-648
  
• Lucas Paletta, Gerald Fritz, Christin Seifert:
  Q-learning of sequential attention for visual object recognition from informative local descriptors. 649-656
  
• Franz Pernkopf, Jeff A. Bilmes:
  Discriminative versus generative parameter and structure learning of Bayesian network classifiers. 657-664
  
• Tadeusz Pietraszek:
  Optimizing abstaining classifiers using ROC analysis. 665-672
  
• Barnabás Póczos, András Lörincz:
  Independent subspace analysis using geodesic spanning trees. 673-680
  
• Ganesh Ramakrishnan, Krishna Prasad Chitrapura, Raghu Krishnapuram, Pushpak Bhattacharyya:
  A model for handling approximate, noisy or incomplete labeling in text classification. 681-688
  
• Carl Edward Rasmussen, Joaquin Quiñonero Candela:
  Healing the relevance vector machine through augmentation. 689-696
  
• Soumya Ray, Mark Craven:
  Supervised versus multiple instance learning: an empirical comparison. 697-704
  
• Soumya Ray, David Page:
  Generalized skewing for functions with continuous and nominal attributes. 705-712
  
• Jason D. M. Rennie, Nathan Srebro:
  Fast maximum margin matrix factorization for collaborative prediction. 713-719
  
• Khashayar Rohanimanesh, Sridhar Mahadevan:
  Coarticulation: an approach for generating concurrent plans in Markov decision processes. 720-727
  
• Bernard Rosell, Lisa Hellerstein, Soumya Ray, David Page:
  Why skewing works: learning difficult Boolean functions with greedy tree learners. 728-735
  
• Dan Roth, Wen-tau Yih:
  Integer linear programming inference for conditional random fields. 736-743
  
• Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor:
  Learning hierarchical multi-category text classification models. 744-751
  
• Jarkko Salojärvi, Kai Puolamäki, Samuel Kaski:
  Expectation maximization algorithms for conditional likelihoods. 752-759
  
• Sajama, Alon Orlitsky:
  Estimating and computing density based distance metrics. 760-767
  
• Sajama, Alon Orlitsky:
  Supervised dimensionality reduction using mixture models. 768-775
  
• Bernhard Schölkopf, Florian Steinke, Volker Blanz:
  Object correspondence as a machine learning problem. 776-783
  
• Fei Sha, Lawrence K. Saul:
  Analysis and extension of spectral methods for nonlinear dimensionality reduction. 784-791
  
• Amnon Shashua, Tamir Hazan:
  Non-negative tensor factorization with applications to statistics and computer vision. 792-799
  
• Sajid M. Siddiqi, Andrew W. Moore:
  Fast inference and learning in large-state-space HMMs. 800-807
  
• Ricardo Silva, Richard Scheines:
  New d-separation identification results for learning continuous latent variable models. 808-815
  
• Özgür Simsek, Alicia P. Wolfe, Andrew G. Barto:
  Identifying useful subgoals in reinforcement learning by local graph partitioning. 816-823
  
• Vikas Sindhwani, Partha Niyogi, Mikhail Belkin:
  Beyond the point cloud: from transductive to semi-supervised learning. 824-831
  
• Rohit Singh, Nathan Palmer, David K. Gifford, Bonnie Berger, Ziv Bar-Joseph:
  Active learning for sampling in time-series experiments with application to gene expression analysis. 832-839
  
• Edward Snelson, Zoubin Ghahramani:
  Compact approximations to Bayesian predictive distributions. 840-847
  
• Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf:
  Large scale genomic sequence SVM classifiers. 848-855
  
• Alexander L. Strehl, Michael L. Littman:
  A theoretical analysis of Model-Based Interval Estimation. 856-863
  
• Qiang Sun, Gerald DeJong:
  Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning. 864-871
  
• Yijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu:
  Unifying the error-correcting and output-code AdaBoost within the margin framework. 872-879
  
• Csaba Szepesvári, Rémi Munos:
  Finite time bounds for sampling based fitted value iteration. 880-887
  
• Brian Tanner, Richard S. Sutton:
  TD(lambda) networks: temporal-difference networks with eligibility traces. 888-895
  
• Benjamin Taskar, Vassil Chatalbashev, Daphne Koller, Carlos Guestrin:
  Learning structured prediction models: a large margin approach. 896-903
  
• Marc Toussaint, Sethu Vijayakumar:
  Learning discontinuities with products-of-sigmoids for switching between local models. 904-911
  
• Ivor W. Tsang, James T. Kwok, Kimo T. Lai:
  Core Vector Regression for very large regression problems. 912-919
  
• Koji Tsuda:
  Propagating distributions on a hypergraph by dual information regularization. 920-927
  
• Sriharsha Veeramachaneni, Diego Sona, Paolo Avesani:
  Hierarchical Dirichlet model for document classification. 928-935
  
• Christian Walder, Olivier Chapelle, Bernhard Schölkopf:
  Implicit surface modelling as an eigenvalue problem. 936-939
  
• Chang Wang, Stephen D. Scott:
  New kernels for protein structural motif discovery and function classification. 940-947
  
• Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng:
  Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. 948-955
  
• Tao Wang, Daniel J. Lizotte, Michael H. Bowling, Dale Schuurmans:
  Bayesian sparse sampling for on-line reward optimization. 956-963
  
• Eric Wiewiora:
  Learning predictive representations from a history. 964-971
  
• David Williams, Xuejun Liao, Ya Xue, Lawrence Carin:
  Incomplete-data classification using logistic regression. 972-979
  
• Britton Wolfe, Michael R. James, Satinder P. Singh:
  Learning predictive state representations in dynamical systems without reset. 980-987
  
• Jianxin Wu, Matthew D. Mullin, James M. Rehg:
  Linear Asymmetric Classifier for cascade detectors. 988-995
  
• Mingrui Wu, Bernhard Schölkopf, Gökhan H. Bakir:
  Building Sparse Large Margin Classifiers. 996-1003
  
• Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel:
  Dirichlet enhanced relational learning. 1004-1011
  
• Kai Yu, Volker Tresp, Anton Schwaighofer:
  Learning Gaussian processes from multiple tasks. 1012-1019
  
• Harry Zhang, Liangxiao Jiang, Jiang Su:
  Augmenting naive Bayes for ranking. 1020-1027
  
• Ding Zhou, Jia Li, Hongyuan Zha:
  A new Mallows distance based metric for comparing clusterings. 1028-1035
  
• Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf:
  Learning from labeled and unlabeled data on a directed graph. 1036-1043
  
• Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Ying Ma:
  2D Conditional Random Fields for Web information extraction. 1044-1051
  
• Xiaojin Zhu, John D. Lafferty:
  Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning. 1052-1059
  
• Alexander Zien, Joaquin Quiñonero Candela:
  Large margin non-linear embedding. 1060-1067