21. ICML 2004: Banff, Alberta, Canada

• Klaus Brinker:
  Active learning of label ranking functions.
  
• Tong Zhang:
  Solving large scale linear prediction problems using stochastic gradient descent algorithms.
  
• Guy Lebanon, John D. Lafferty:
  Hyperplane margin classifiers on the multinomial manifold.
  
• Lourdes Peña Castillo, Stefan Wrobel:
  A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning.
  
• Jian Zhang, Yiming Yang:
  Probabilistic score estimation with piecewise logistic regression.
  
• Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier:
  Boosting grammatical inference with confidence oracles.
  
• John D. Lafferty, Xiaojin Zhu, Yan Liu:
  Kernel conditional random fields: representation and clique selection.
  
• Remco R. Bouckaert:
  Estimating replicability of classifier learning experiments.
  
• Daniel Grossman, Pedro Domingos:
  Learning Bayesian network classifiers by maximizing conditional likelihood.
  
• Daniil Ryabko:
  Online learning of conditionally I.I.D. data.
  
• Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun:
  Support vector machine learning for interdependent and structured output spaces.
  
• Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
  Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model.
  
• Ankur Agarwal, Bill Triggs:
  Learning to track 3D human motion from silhouettes.
  
• Nir Krause, Yoram Singer:
  Leveraging the margin more carefully.
  
• Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul:
  Learning a kernel matrix for nonlinear dimensionality reduction.
  
• Daan Wierstra, Marco Wiering:
  Utile distinction hidden Markov models.
  
• Jieping Ye:
  Generalized low rank approximations of matrices.
  
• Jieping Ye, Ravi Janardan, Qi Li, Haesun Park:
  Feature extraction via generalized uncorrelated linear discriminant analysis.
  
• Hieu Tat Nguyen, Arnold W. M. Smeulders:
  Active learning using pre-clustering.
  
• Ulf Brefeld, Tobias Scheffer:
  Co-EM support vector learning.
  
• Vincent Conitzer, Tuomas Sandholm:
  Communication complexity as a lower bound for learning in games.
  
• Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
  Margin based feature selection - theory and algorithms.
  
• Özgür Simsek, Andrew G. Barto:
  Using relative novelty to identify useful temporal abstractions in reinforcement learning.
  
• Wei Chu, Zoubin Ghahramani, David L. Wild:
  A graphical model for protein secondary structure prediction.
  
• Shie Mannor, Ishai Menache, Amit Hoze, Uri Klein:
  Dynamic abstraction in reinforcement learning via clustering.
  
• George Forman:
  A pitfall and solution in multi-class feature selection for text classification.
  
• Odest Chadwicke Jenkins, Maja J. Mataric:
  A spatio-temporal extension to Isomap nonlinear dimension reduction.
  
• Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal:
  The Bayesian backfitting relevance vector machine.
  
• Michael R. James, Satinder P. Singh:
  Learning and discovery of predictive state representations in dynamical systems with reset.
  
• Mikhail Bilenko, Sugato Basu, Raymond J. Mooney:
  Integrating constraints and metric learning in semi-supervised clustering.
  
• Sheng Gao, Wen Wu, Chin-Hui Lee, Tat-Seng Chua:
  A MFoM learning approach to robust multiclass multi-label text categorization.
  
• Jianguo Lee, Jingdong Wang, Changshui Zhang, Zhaoqi Bian:
  Probabilistic tangent subspace: a unified view.
  
• Eibe Frank, Stefan Kramer:
  Ensembles of nested dichotomies for multi-class problems.
  
• Yongdai Kim, Jinseog Kim:
  Gradient LASSO for feature selection.
  
• Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu:
  Learning large margin classifiers locally and globally.
  
• Alan Herschtal, Bhavani Raskutti:
  Optimising area under the ROC curve using gradient descent.
  
• Robert B. Gramacy, Herbert K. H. Lee, William G. Macready:
  Parameter space exploration with Gaussian process trees.
  
• Zhihua Zhang, Dit-Yan Yeung, James T. Kwok:
  Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm.
  
• Charles X. Ling, Qiang Yang, Jianning Wang, Shichao Zhang:
  Decision trees with minimal costs.
  
• Rong Jin, Huan Liu:
  Robust feature induction for support vector machines.
  
• Cristian Sminchisescu, Allan D. Jepson:
  Generative modeling for continuous non-linearly embedded visual inference.
  
• Duncan Potts:
  Incremental learning of linear model trees.
  
• Saher Esmeir, Shaul Markovitch:
  Lookahead-based algorithms for anytime induction of decision trees.
  
• Ofer Dekel, Joseph Keshet, Yoram Singer:
  Large margin hierarchical classification.
  
• Jaakko Peltonen, Janne Sinkkonen, Samuel Kaski:
  Sequential information bottleneck for finite data.
  
• Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng:
  Online and batch learning of pseudo-metrics.
  
• Aleks Jakulin, Ivan Bratko:
  Testing the significance of attribute interactions.
  
• Antonio Bahamonde, Gustavo F. Bayón, Jorge Díez, José Ramón Quevedo, Oscar Luaces, Juan José del Coz, Jaime Alonso, Félix Goyache:
  Feature subset selection for learning preferences: a case study.
  
• Jong-Hoon Ahn, Seungjin Choi, Jong-Hoon Oh:
  A multiplicative up-propagation algorithm.
  
• Ted Scully, Michael G. Madden, Gerard Lyons:
  Coalition calculation in a dynamic agent environment.
  
• Malcolm J. A. Strens:
  Efficient hierarchical MCMC for policy search.
  
• Neil D. Lawrence, John C. Platt:
  Learning to learn with the informative vector machine.
  
• Hisashi Kashima, Yuta Tsuboi:
  Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs.
  
• Eduardo F. Morales, Claude Sammut:
  Learning to fly by combining reinforcement learning with behavioural cloning.
  
• Prem Melville, Raymond J. Mooney:
  Diverse ensembles for active learning.
  
• Roberto Esposito, Lorenza Saitta:
  A Monte Carlo analysis of ensemble classification.
  
• Ulrich Rückert, Stefan Kramer:
  Towards tight bounds for rule learning.
  
• Evgeniy Gabrilovich, Shaul Markovitch:
  Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5.
  
• Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall:
  Boosting margin based distance functions for clustering.
  
• Artur Merke, Ralf Schoknecht:
  Convergence of synchronous reinforcement learning with linear function approximation.
  
• Hong Chang, Dit-Yan Yeung:
  Locally linear metric adaptation for semi-supervised clustering.
  
• Soumya Ray, David Page:
  Sequential skewing: an improved skewing algorithm.
  
• Ting Su, Jennifer G. Dy:
  Automated hierarchical mixtures of probabilistic principal component analyzers.
  
• Justin Basilico, Thomas Hofmann:
  Unifying collaborative and content-based filtering.
  
• César Ferri, Peter A. Flach, José Hernández-Orallo:
  Delegating classifiers.
  
• Max Welling, Michal Rosen-Zvi, Yee Whye Teh:
  Approximate inference by Markov chains on union spaces.
  
• Annalisa Appice, Michelangelo Ceci, Simon Rawles, Peter A. Flach:
  Redundant feature elimination for multi-class problems.
  
• Nicolas Baskiotis, Michèle Sebag:
  C4.5 competence map: a phase transition-inspired approach.
  
• Koby Crammer, Gal Chechik:
  A needle in a haystack: local one-class optimization.
  
• Saharon Rosset:
  Model selection via the AUC.
  
• Kristian Kersting, Martijn van Otterlo, Luc De Raedt:
  Bellman goes relational.
  
• Shie Mannor, Duncan Simester, Peng Sun, John N. Tsitsiklis:
  Bias and variance in value function estimation.
  
• Rómer Rosales, Kannan Achan, Brendan J. Frey:
  Learning to cluster using local neighborhood structure.
  
• Tao Li, Sheng Ma, Mitsunori Ogihara:
  Entropy-based criterion in categorical clustering.
  
• Qingping Tao, Stephen D. Scott, N. V. Vinodchandran, Thomas Takeo Osugi:
  SVM-based generalized multiple-instance learning via approximate box counting.
  
• Anna Goldenberg, Andrew Moore:
  Tractable learning of large Bayes net structures from sparse data.
  
• Chris H. Q. Ding, Xiaofeng He:
  Linearized cluster assignment via spectral ordering.
  
• Chris H. Q. Ding, Xiaofeng He:
  K-means clustering via principal component analysis.
  
• Glenn Fung, Murat Dundar, Jinbo Bi, R. Bharat Rao:
  A fast iterative algorithm for fisher discriminant using heterogeneous kernels.
  
• Jelle R. Kok, Nikos A. Vlassis:
  Sparse cooperative Q-learning.
  
• Matthew R. Rudary, Satinder P. Singh, Martha E. Pollack:
  Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning.
  
• Steven J. Phillips, Miroslav Dudík, Robert E. Schapire:
  A maximum entropy approach to species distribution modeling.
  
• Austin I. Eliazar, Ronald Parr:
  Learning probabilistic motion models for mobile robots.
  
• Xiaoli Zhang Fern, Carla E. Brodley:
  Solving cluster ensemble problems by bipartite graph partitioning.
  
• Ronan Collobert, Samy Bengio:
  Links between perceptrons, MLPs and SVMs.
  
• Sander M. Bohte, Markus Breitenbach, Gregory Z. Grudic:
  Nonparametric classification with polynomial MPMC cascades.
  
• Jihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schölkopf:
  A kernel view of the dimensionality reduction of manifolds.
  
• Yuan (Alan) Qi, Thomas P. Minka, Rosalind W. Picard, Zoubin Ghahramani:
  Predictive automatic relevance determination by expectation propagation.
  
• Sharlee Climer, Weixiong Zhang:
  Take a walk and cluster genes: a TSP-based approach to optimal rearrangement clustering.
  
• Alan Fern, Robert Givan:
  Relational sequential inference with reliable observations.
  
• Douglas P. Hardin, Ioannis Tsamardinos, Constantin F. Aliferis:
  A theoretical characterization of linear SVM-based feature selection.
  
• Charles A. Sutton, Khashayar Rohanimanesh, Andrew McCallum:
  Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data.
  
• Eric P. Xing, Roded Sharan, Michael I. Jordan:
  Bayesian haplo-type inference via the dirichlet process.
  
• Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan:
  Multiple kernel learning, conic duality, and the SMO algorithm.
  
• Bianca Zadrozny:
  Learning and evaluating classifiers under sample selection bias.
  
• Tony Jebara:
  Multi-task feature and kernel selection for SVMs.
  
• Volkan Vural, Jennifer G. Dy:
  A hierarchical method for multi-class support vector machines.
  
• Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov:
  Training conditional random fields via gradient tree boosting.
  
• Avrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy:
  Semi-supervised learning using randomized mincuts.
  
• Pieter Abbeel, Andrew Y. Ng:
  Apprenticeship learning via inverse reinforcement learning.
  
• Arindam Banerjee, Inderjit S. Dhillon, Joydeep Ghosh, Srujana Merugu:
  An information theoretic analysis of maximum likelihood mixture estimation for exponential families.
  
• Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, Alex Ksikes:
  Ensemble selection from libraries of models.
  
• Yasemin Altun, Thomas Hofmann, Alex J. Smola:
  Gaussian process classification for segmenting and annotating sequences.
  
• Corinna Cortes, Mehryar Mohri:
  Distribution kernels based on moments of counts.
  
• Pengcheng Wu, Thomas G. Dietterich:
  Improving SVM accuracy by training on auxiliary data sources.
  
• Benjamin M. Marlin, Richard S. Zemel:
  The multiple multiplicative factor model for collaborative filtering.
  
• XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan:
  Decentralized detection and classification using kernel methods.
  
• David M. Blei, Michael I. Jordan:
  Variational methods for the Dirichlet process.
  
• David Wingate, Kevin D. Seppi:
  P3VI: a partitioned, prioritized, parallel value iterator.
  
• Matthew Rosencrantz, Geoffrey J. Gordon, Sebastian Thrun:
  Learning low dimensional predictive representations.
  
• Kristina Toutanova, Christopher D. Manning, Andrew Y. Ng:
  Learning random walk models for inducing word dependency distributions.
  
• Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola:
  Learning with non-positive kernels.
  
• Benjamin Taskar, Vassil Chatalbashev, Daphne Koller:
  Learning associative Markov networks.
  
• Csaba Szepesvári, William D. Smart:
  Interpolation-based Q-learning.
  
• Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, Jean-Philippe Vert:
  Extensions of marginalized graph kernels.
  
• Cholwich Nattee, Sukree Sinthupinyo, Masayuki Numao, Takashi Okada:
  Learning first-order rules from data with multiple parts: applications on mining chemical compound data.
  
• Moshe Koppel, Jonathan Schler:
  Authorship verification as a one-class classification problem.
  

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