Empirical Inference

Part I. History of Statistical Learning Theory

1. In Hindsight: Doklady Akademii Nauk SSSR, 181(4), 1968
Léon Bottou

2. On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities
Vladimir N. Vapnik, Alexey Ya. Chervonenkis

3. Early History of Support Vector Machines
Alexey Ya. Chervonenkis

Part II. Theory and Practice of Statistical Learning Theory

4. Some Remarks on the Statistical Analysis of SVMs and Related Methods
Ingo Steinwart

5. Explaining AdaBoost
Robert E. Schapire

6. On the Relations and Differences Between Popper Dimension, Exclusion Dimension and VC-Dimension
Yevgeny Seldin, Bernhard Schölkopf

7. On Learnability, Complexity and Stability
Silvia Villa, Lorenzo Rosasco, Tomaso Poggio

8. Loss Functions
Robert C. Williamson

9. Statistical Learning Theory in Practice
Jason Weston

10. PAC-Bayesian Theory
David McAllester, Takintayo Akinbiyi

11. Kernel Ridge Regression
Vladimir Vovk

12. Multi-task Learning for Computational Biology: Overview and Outlook
Christian Widmer, Marius Kloft, Gunnar Rätsch

13. Semi-supervised Learning in Causal and Anticausal Settings
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris Mooij

14. Strong Universal Consistent Estimate of the Minimum Mean Squared Error
Luc Devroye, Paola G. Ferrario, László Györfi, Harro Walk

15. The Median Hypothesis
Ran Gilad-Bachrach, Chris J. C. Burges

16. Efficient Transductive Online Learning via Randomized Rounding
Nicolò Cesa-Bianchi, Ohad Shamir

17. Pivotal Estimation in High-Dimensional Regression via Linear Programming
Eric Gautier, Alexandre B. Tsybakov

18. On Sparsity Inducing Regularization Methods for Machine Learning
Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil

19. Sharp Oracle Inequalities in Low Rank Estimation
Vladimir Koltchinskii

20. On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods
Andreas Christmann, Robert Hable

21. Kernels, Pre-images and Optimization
John C. Snyder, Sebastian Mika, Kieron Burke, Klaus-Robert Müller

22. Efficient Learning of Sparse Ranking Functions
Mark Stevens, Samy Bengio, Yoram Singer

23. Direct Approximation of Divergences Between Probability Distributions
Masashi Sugiyama

Keywords: Computer Science, Artificial Intelligence (incl. Robotics), Statistical Theory and Methods, Probability and Statistics in Computer Science, Optimization