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ORCIDHonor of Vladimir Vapnik 2013
- Bernhard Schölkopf, Zhiyuan Luo, Vladimir Vovk
:
Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik. Springer 2013, ISBN 978-3-642-41135-9
History of Statistical Learning Theory
- Léon Bottou:
In Hindsight: Doklady Akademii Nauk SSSR, 181(4), 1968. 3-5 - Vladimir Naumovich Vapnik, Alexey Ya. Chervonenkis:
On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities. 7-12 - Alexey Ya. Chervonenkis:
Early History of Support Vector Machines. 13-20
Theory and Practice of Statistical Learning Theory
- Ingo Steinwart:
Some Remarks on the Statistical Analysis of SVMs and Related Methods. 25-36 - Robert E. Schapire:
Explaining AdaBoost. 37-52 - Yevgeny Seldin, Bernhard Schölkopf:
On the Relations and Differences Between Popper Dimension, Exclusion Dimension and VC-Dimension. 53-57 - Silvia Villa, Lorenzo Rosasco, Tomaso A. Poggio:
On Learnability, Complexity and Stability. 59-69 - Robert C. Williamson:
Loss Functions. 71-80 - Jason Weston:
Statistical Learning Theory in Practice. 81-93 - David McAllester, Takintayo Akinbiyi:
PAC-Bayesian Theory. 95-103 - Vladimir Vovk:
Kernel Ridge Regression. 105-116 - Christian Widmer, Marius Kloft, Gunnar Rätsch:
Multi-task Learning for Computational Biology: Overview and Outlook. 117-127 - Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
Semi-supervised Learning in Causal and Anticausal Settings. 129-141 - Luc Devroye, Paola G. Ferrario, László Györfi, Harro Walk:
Strong Universal Consistent Estimate of the Minimum Mean Squared Error. 143-160 - Ran Gilad-Bachrach, Christopher J. C. Burges:
The Median Hypothesis. 161-175 - Nicolò Cesa-Bianchi, Ohad Shamir:
Efficient Transductive Online Learning via Randomized Rounding. 177-194 - Eric Gautier, Alexandre B. Tsybakov:
Pivotal Estimation in High-Dimensional Regression via Linear Programming. 195-204 - Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil:
On Sparsity Inducing Regularization Methods for Machine Learning. 205-216 - Vladimir Koltchinskii:
Sharp Oracle Inequalities in Low Rank Estimation. 217-230 - Andreas Christmann, Robert Hable:
On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods. 231-244 - John C. Snyder, Sebastian Mika, Kieron Burke, Klaus-Robert Müller:
Kernels, Pre-images and Optimization. 245-259 - Mark Stevens, Samy Bengio, Yoram Singer:
Efficient Learning of Sparse Ranking Functions. 261-271 - Masashi Sugiyama:
Direct Approximation of Divergences Between Probability Distributions. 273-283
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