ML Anthology

Vapnik, Vladimir

37 publications

MLJ 2019 Rethinking Statistical Learning Theory: Learning Using Statistical Invariants Vladimir Vapnik, Rauf Izmailov
JMLR 2016 Synergy of Monotonic Rules Vladimir Vapnik, Rauf Izmailov
ICLR 2016 Unifying Distillation and Privileged Information David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik
JMLR 2015 Learning Using Privileged Information: Similarity Control and Knowledge Transfer Vladimir Vapnik, Rauf Izmailov
JMLR 2015 V-Matrix Method of Solving Statistical Inference Problems Vladimir Vapnik, Rauf Izmailov
NeurIPS 2010 On the Theory of Learnining with Privileged Information Dmitry Pechyony, Vladimir Vapnik
ECML-PKDD 2008 Large Margin vs. Large Volume in Transductive Learning Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
MLJ 2008 Large Margin vs. Large Volume in Transductive Learning Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
ICML 2006 Inference with the Universum Jason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik
NeurIPS 2004 Parallel Support Vector Machines: The Cascade SVM Hans P. Graf, Eric Cosatto, Léon Bottou, Igor Dourdanovic, Vladimir Vapnik
COLT 2003 Learning with Rigorous Support Vector Machines Jinbo Bi, Vladimir Vapnik
MLJ 2002 Choosing Multiple Parameters for Support Vector Machines Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
MLJ 2002 Gene Selection for Cancer Classification Using Support Vector Machines Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik
NeurIPS 2002 Kernel Dependency Estimation Jason Weston, Olivier Chapelle, Vladimir Vapnik, André Elisseeff, Bernhard Schölkopf
MLJ 2002 Model Selection for Small Sample Regression Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio
JMLR 2001 Support Vector Clustering (Kernel Machines Section) Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
NeurIPS 2000 A Support Vector Method for Clustering Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
NeCo 2000 Bounds on Error Expectation for Support Vector Machines Vladimir Vapnik, Olivier Chapelle
NeurIPS 2000 Feature Selection for SVMs Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso Poggio, Vladimir Vapnik
NeurIPS 2000 Vicinal Risk Minimization Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik
NeurIPS 1999 Model Selection for Support Vector Machines Olivier Chapelle, Vladimir Vapnik
NeurIPS 1999 Support Vector Method for Multivariate Density Estimation Vladimir Vapnik, Sayan Mukherjee
NeurIPS 1999 Transductive Inference for Estimating Values of Functions Olivier Chapelle, Vladimir Vapnik, Jason Weston
UAI 1998 Learning by Transduction Alexander Gammerman, Volodya Vovk, Vladimir Vapnik
NeurIPS 1997 Prior Knowledge in Support Vector Kernels Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir Vapnik
ICML 1996 Statistical Theory of Generalization (Abstract) Vladimir Vapnik
NeurIPS 1996 Support Vector Method for Function Approximation, Regression Estimation and Signal Processing Vladimir Vapnik, Steven E. Golowich, Alex J. Smola
NeurIPS 1996 Support Vector Regression Machines Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik
MLJ 1995 Support-Vector Networks Corinna Cortes, Vladimir Vapnik
NeCo 1994 Boosting and Other Ensemble Methods Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik
ICML 1994 Boosting and Other Machine Learning Algorithms Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik
NeCo 1994 Measuring the VC-Dimension of a Learning Machine Vladimir Vapnik, Esther Levin, Yann LeCun
NeurIPS 1993 Learning Curves: Asymptotic Values and Rate of Convergence Corinna Cortes, L. D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker
NeCo 1993 Local Algorithms for Pattern Recognition and Dependencies Estimation Vladimir Vapnik, Léon Bottou
COLT 1992 A Training Algorithm for Optimal Margin Classifiers Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik
NeCo 1992 Local Learning Algorithms Léon Bottou, Vladimir Vapnik
COLT 1989 Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures) Vladimir Vapnik