Volume 9, 2008

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  Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller: Max-margin Classification of Data with Absent Features. 1-21
• 
  Konrad Rieck, Pavel Laskov: Linear-Time Computation of Similarity Measures for Sequential Data. 23-48
• 
  Gustavo Camps-Valls, Juan Gutierrez, Gabriel Gómez-Pérez, Jesus Malo: On the Suitable Domain for SVM Training in Image Coding. 49-66
• 
  Vojtech Franc, Bogdan Savchynskyy: Discriminative Learning of Max-Sum Classifiers. 67-104
• 
  Falk-Florian Henrich, Klaus Obermayer: Active Learning by Spherical Subdivision. 105-130
• 
  David Mease, Abraham J. Wyner: Evidence Contrary to the Statistical View of Boosting. 131-156
• 
  Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi: Optimization Techniques for Semi-Supervised Support Vector Machines. 203-233
• 
  Andreas Krause, Ajit Paul Singh, Carlos Guestrin: Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies. 235-284
• 
  Hsuan-Tien Lin, Ling Li: Support Vector Machinery for Infinite Ensemble Learning. 285-312
• 
  Suhrid Balakrishnan, David Madigan: Algorithms for Sparse Linear Classifiers in the Massive Data Setting. 313-337
• 
  Eyal Krupka, Naftali Tishby: Generalization from Observed to Unobserved Features by Clustering. 339-370
• 
  Glenn Shafer, Vladimir Vovk: A Tutorial on Conformal Prediction. 371-421
• 
  Liviu Panait, Karl Tuyls, Sean Luke: Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective. 423-457
• 
  Xianchao Xie, Zhi Geng: A Recursive Method for Structural Learning of Directed Acyclic Graphs. 459-483
• 
  Onureena Banerjee, Laurent El Ghaoui, Alexandre d'Aspremont: Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data. 485-516
• 
  Jieping Ye: Comments on the Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion. 517-519
• 
  Bo Jiang, Xuegong Zhang, Tianxi Cai: Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers. 521-540
• 
  Gerda Claeskens, Christophe Croux, Johan Van Kerckhoven: An Information Criterion for Variable Selection in Support Vector Machines. 541-558
• 
  Gemma C. Garriga, Petra Kralj, Nada Lavrac: Closed Sets for Labeled Data. 559-580
• 
  Giorgio Corani, Marco Zaffalon: Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2. 581-621
• 
  Stefan Klanke, Sethu Vijayakumar, Stefan Schaal: A Library for Locally Weighted Projection Regression. 623-626
• 
  Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi: Trust Region Newton Method for Logistic Regression. 627-650
• 
  Shann-Ching Chen, Geoffrey J. Gordon, Robert F. Murphy: Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns. 651-682
• 
  Sung Wook Yoon, Alan Fern, Robert Givan: Learning Control Knowledge for Forward Search Planning. 683-718
• 
  Jieping Ye, Shuiwang Ji, Jianhui Chen: Multi-class Discriminant Kernel Learning via Convex Programming. 719-758
• 
  Matthias W. Seeger: Bayesian Inference and Optimal Design for the Sparse Linear Model. 759-813
• 
  Rémi Munos, Csaba Szepesvári: Finite-Time Bounds for Fitted Value Iteration. 815-857
• 
  Eric Bax, Augusto Callejas: An Error Bound Based on a Worst Likely Assignment. 859-891
• 
  Mathias Drton, Thomas S. Richardson: Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models. 893-914
• 
  Andreas Christmann, Arnout Van Messem: Bouligand Derivatives and Robustness of Support Vector Machines for Regression. 915-936
• 
  Faustino J. Gomez, Jürgen Schmidhuber, Risto Miikkulainen: Accelerated Neural Evolution through Cooperatively Coevolved Synapses. 937-965
• 
  Tianjiao Chu, Clark Glymour: Search for Additive Nonlinear Time Series Causal Models. 967-991
• 
  Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers: Shark. 993-996
• 
  Elena Marchiori: Hit Miss Networks with Applications to Instance Selection. 997-1017
• 
  Francis R. Bach: Consistency of Trace Norm Minimization. 1019-1048
• 
  Andreas Maurer: Learning Similarity with Operator-valued Large-margin Classifiers. 1049-1082
• 
  Sivan Sabato, Shai Shalev-Shwartz: Ranking Categorical Features Using Generalization Properties. 1083-1114
• 
  Zach Jorgensen, Yan Zhou, W. Meador Inge: A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters. 1115-1146
• 
  Matthias W. Seeger: Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods. 1147-1178
• 
  Francis R. Bach: Consistency of the Group Lasso and Multiple Kernel Learning. 1179-1225
• 
  Jörg Lücke, Maneesh Sahani: Maximal Causes for Non-linear Component Extraction. 1227-1267
• 
  Alexandre d'Aspremont, Francis Bach, Laurent El Ghaoui: Optimal Solutions for Sparse Principal Component Analysis. 1269-1294
• 
  Jean-Philippe Pellet, André Elisseeff: Using Markov Blankets for Causal Structure Learning. 1295-1342
• 
  Ja-Yong Koo, Yoonkyung Lee, Yuwon Kim, Changyi Park: A Bahadur Representation of the Linear Support Vector Machine. 1343-1368
• 
  Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin: Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines. 1369-1398
• 
  Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer: Online Learning of Complex Prediction Problems Using Simultaneous Projections. 1399-1435
• 
  Jiji Zhang: Causal Reasoning with Ancestral Graphs. 1437-1474
• 
  Ashwin Srinivasan, Ross D. King: Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming. 1475-1533
• 
  Manu Chhabra, Robert A. Jacobs: Learning to Combine Motor Primitives Via Greedy Additive Regression. 1535-1558
• 
  Sébastien Loustau: Aggregation of SVM Classifiers Using Sobolev Spaces. 1559-1582
• 
  Jun Zhu, Zaiqing Nie, Bo Zhang, Ji-Rong Wen: Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction. 1583-1614
• 
  Andrea Caponnetto, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying: Universal Multi-Task Kernels. 1615-1646
• 
  Arnak S. Dalalyan, Anatoli Juditsky, Vladimir Spokoiny: A New Algorithm for Estimating the Effective Dimension-Reduction Subspace. 1647-1678
• 
  Balázs Csanád Csáji, László Monostori: Value Function Based Reinforcement Learning in Changing Markovian Environments. 1679-1709
• 
  Arthur D. Szlam, Mauro Maggioni, Ronald R. Coifman: Regularization on Graphs with Function-adapted Diffusion Processes. 1711-1739
• 
  Eric Bax: Nearly Uniform Validation Improves Compression-Based Error Bounds. 1741-1755
• 
  Koby Crammer, Michael Kearns, Jennifer Wortman: Learning from Multiple Sources. 1757-1774
• 
  Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett: Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks. 1775-1822
• 
  Peter L. Bartlett, Marten H. Wegkamp: Classification with a Reject Option using a Hinge Loss. 1823-1840
• 
  Leonor Becerra-Bonache, Colin de la Higuera, Jean-Christophe Janodet, Frédéric Tantini: Learning Balls of Strings from Edit Corrections. 1841-1870
• 
  Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin: LIBLINEAR: A Library for Large Linear Classification. 1871-1874
• 
  Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert Müller: On Relevant Dimensions in Kernel Feature Spaces. 1875-1908
• 
  Yair Goldberg, Alon Zakai, Dan Kushnir, Yaacov Ritov: Manifold Learning: The Price of Normalization. 1909-1939
• 
  Ilya Shpitser, Judea Pearl: Complete Identification Methods for the Causal Hierarchy. 1941-1979
• 
  Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing: Mixed Membership Stochastic Blockmodels. 1981-2014
• 
  Gérard Biau, Luc Devroye, Gábor Lugosi: Consistency of Random Forests and Other Averaging Classifiers. 2015-2033

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