- 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 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.**