V. Koltchinskii

发表

Empirical margin distributions and bounding the generalization error of combined classifiers

V. Koltchinskii, D. Panchenko, 2002, math/0405343.

The Dantzig selector and sparsity oracle inequalities

V. Koltchinskii, 2009, 0909.0861.

Oracle inequalities in empirical risk minimization and sparse recovery problems

V. Koltchinskii, École d'été de probabilités de Saint-Flour, 2011 .

Random matrix approximation of spectra of integral operators

V. Koltchinskii, E. Giné, 2000 .

Bounding the smallest singular value of a random matrix without concentration

V. Koltchinskii, S. Mendelson, 2013, 1312.3580.

Concentration inequalities and asymptotic results for ratio type empirical processes

V. Koltchinskii, Evarist Gin'e, 2006, math/0606788.

Rademacher Processes and Bounding the Risk of Function Learning

V. Koltchinskii, D. Panchenko, 2004, math/0405338.

Local Rademacher complexities and oracle inequalities in risk minimization

V. Koltchinskii, 2006, 0708.0083.

Rejoinder: Local Rademacher complexities and oracle inequalities in risk minimization

V. Koltchinskii, 2006, 0708.0135.

Empirical graph Laplacian approximation of Laplace–Beltrami operators: Large sample results

V. Koltchinskii, Evarist Gin'e, 2006, math/0612777.

SPARSITY IN MULTIPLE KERNEL LEARNING

V. Koltchinskii, M. Yuan, 2010, 1211.2998.

Sparsity in penalized empirical risk minimization

V. Koltchinskii, 2009 .

Rademacher penalties and structural risk minimization

Vladimir Koltchinskii, V. Koltchinskii, 2001, IEEE Trans. Inf. Theory.

Concentration inequalities and moment bounds for sample covariance operators

V. Koltchinskii, Karim Lounici, 2014, 1405.2468.

Low Rank Estimation of Similarities on Graphs

V. Koltchinskii, P. Rangel, 2012, 1205.1868.

Asymptotics of Spectral Projections of Some Random Matrices Approximating Integral Operators

V. Koltchinskii, 1998 .

A note on the richness of convex hulls of VC classes

G. Lugosi, V. Koltchinskii, S. Mendelson, 2003 .

Some New Bounds on the Generalization Error of Combined Classifiers

Dmitry Panchenko, Fernando Lozano, Vladimir Koltchinskii, 2000, NIPS.

Complexities of convex combinations and bounding the generalization error in classification

V. Koltchinskii, D. Panchenko, 2004, math/0405356.

Optimal estimation of low rank density matrices

Dong Xia, Vladimir Koltchinskii, V. Koltchinskii, 2015, J. Mach. Learn. Res..

A remark on low rank matrix recovery and noncommutative Bernstein type inequalities

V. Koltchinskii, 2013 .

Estimation of low rank density matrices: bounds in Schatten norms and other distances

V. Koltchinskii, Dong Xia, 2016, 1604.04600.

High Dimensional Probability

V. Koltchinskii, J. Zinn, Wenbo V. Li, 2006, math/0612726.

Empirical geometry of multivariate data: a deconvolution approach

V. Koltchinskii, 2000 .

Sparse recovery in convex hulls via entropy penalization

V. Koltchinskii, 2009, 0905.2078.

Some Local Measures of Complexity of Convex Hulls and Generalization Bounds

Dmitry Panchenko, Olivier Bousquet, Vladimir Koltchinskii, 2002, COLT.

Statistical learning methods in linear algebra and control problems: the example of finite-time control of uncertain linear systems

V. Koltchinskii, C. Abdallah, P. Dorato, 2002 .

Weighted uniform consistency of kernel density estimators

Joel Zinn, Vladimir Koltchinskii, V. Koltchinskii, 2004 .

M-estimation, convexity and quantiles

V. Koltchinskii, 1997 .

Application of Statistical Learning Control to the Design of a Fixed-Order Controller for a Flexible Beam

V. Koltchinskii, C. Abdallah, M. Ariola, 2012 .

The mathematical work of Evarist Giné

R. Nickl, S. Geer, J. Wellner, 2016 .

Bootstrap by Sequential Resampling.

C. R. Rao, V. Koltchinskii, P. Pathak, 1997 .

Testing for Spherical Symmetry of a Multivariate Distribution

V. Koltchinskii, Lang Li, 1998 .

Bounding the generalization error of convex combinations of classifiers: balancing the dimensionality and the margins

V. Koltchinskii, D. Panchenko, Fernando Lozano, 2004, math/0405345.

Ratio Limit Theorems for Empirical Processes

Jon A. Wellner, Vladimir Koltchinskii, Evarist Giné, 2003 .

Perturbation of linear forms of singular vectors under Gaussian noise

Dong Xia, Vladimir Koltchinskii, V. Koltchinskii, 2015 .

$L_1$-Penalization in Functional Linear Regression with Subgaussian Design

Vladimir Koltchinskii, Stanislav Minsker, V. Koltchinskii, 2013, 1307.8137.

Kernel density estimators: convergence in distribution for weighted sup-norms

Vladimir Koltchinskii, Lyudmila Sakhanenko, Evarist Giné, 2004 .

Asymptotically efficient estimation of smooth functionals of covariance operators

V. Koltchinskii, 2017, Journal of the European Mathematical Society.

fMRI pattern classification using neuroanatomically constrained boosting

Manel Martínez-Ramón, Stefan Posse, Gregory L. Heileman, 2006, NeuroImage.

Nonlinear transformations of empirical processes: functional inverses and Bahadur-Kiefer representat

V. Koltchinskii, 1994 .

Nuclear norm penalization and optimal rates for noisy low rank matrix completion

V. Koltchinskii, A. Tsybakov, Karim Lounici, 2010, 1011.6256.

Estimation of Low-Rank Covariance Function

V. Koltchinskii, A. Tsybakov, A. Tsybakov, 2015, 1504.03009.

FLA BOUNDS ON MARGIN DISTRIBUTIONS IN LEARNING PROBLEMS

V. Koltchinskii, 2003 .

Rademacher Penalties and Structural Risk Minimization Vladimir Koltchinskii C Opt := I Fyj6 =ic(xj)g

V. Koltchinskii, 1999 .

Estimation of smooth functionals of covariance operators: jackknife bias reduction and bounds in terms of effective rank

V. Koltchinskii, 2022, 2205.10280.

Discussions of boosting papers, and rejoinders

Trevor Hastie, Peter J. Bickel, Robert Tibshirani, 2004 .

Weighted uniform consistency of kernel density estimators

V. Koltchinskii, E. Giné, J. Zinn, 2004, math/0410170.

Concentration inequalities and asymptotic results for ratio type empirical processes

V. Koltchinskii, Evarist Gin'e, 2006, math/0606788.

Empirical graph Laplacian approximation of Laplace–Beltrami operators: Large sample results

V. Koltchinskii, Evarist Gin'e, 2006, math/0612777.

The Dantzig selector and sparsity oracle inequalities

V. Koltchinskii, 2009, 0909.0861.

Rademacher Processes and Bounding the Risk of Function Learning

V. Koltchinskii, D. Panchenko, 2004, math/0405338.

High Dimensional Probability

V. Koltchinskii, J. Zinn, Wenbo V. Li, 2006, math/0612726.

Rademacher Processes and Bounding the Risk of Function Learning

V. Koltchinskii, D. Panchenko, 2004, math/0405338.

Complexities of convex combinations and bounding the generalization error in classification

V. Koltchinskii, D. Panchenko, 2004, math/0405356.

Some Local Measures of Complexity of Convex Hulls and Generalization Bounds

V. Koltchinskii, D. Panchenko, O. Bousquet, 2002, COLT.

Optimal Exponential Bounds on the Accuracy of Classification

V. Koltchinskii, A. Tsybakov, G. Kerkyacharian, 2014, Constructive approximation.

Low Rank Matrix Recovery: Nuclear Norm Penalization

V. Koltchinskii, 2011 .

Von Neumann Entropy Penalization and Low Rank Matrix Estimation

V. Koltchinskii, 2010, 1009.2439.

Estimation of smooth functionals of covariance operators: jackknife bias reduction and bounds in terms of effective rank

V. Koltchinskii, 2022, 2205.10280.

Convergence in Distribution of Self-Normalized Sup-Norms of Kernel Density Estimators

V. Koltchinskii, E. Giné, L. Sakhanenko, 2003 .

Functional estimation in log-concave location families

Martin Wahl, Vladimir Koltchinskii, V. Koltchinskii, 2021, 2108.00263.

ASYMPTOTIC EFFICIENCY IN HIGH-DIMENSIONAL COVARIANCE ESTIMATION

V. Koltchinskii, 2019, Proceedings of the International Congress of Mathematicians (ICM 2018).

High dimensional probability : proceedings of the Fourth International Conference

Evarist Giné, Joel Zinn, Vladimir Koltchinskii, 2006 .

Sparsity in high-dimensional learning problems

Vladimir Koltchinskii, Ming Yuan, V. Koltchinskii, 2010 .

A Note on the Asymptotic Distribution of Berk—Jones Type Statistics under the Null Hypothesis

Jon A. Wellner, Vladimir Koltchinskii, J. Wellner, 2003 .

Three papers on boosting: An introduction

Vladimir Koltchinskii, V. Koltchinskii, B. Yu, 2003 .

Efficient estimation of linear functionals of principal components

Vladimir Koltchinskii, Richard Nickl, Matthias Loffler, 2017, The Annals of Statistics.

Asymptotics and Concentration Bounds for Spectral Projectors of Sample Covariance} \runtitle{Asymptotics and concentration of spectral projectors

V. Koltchinskii, Karim Lounici, 2014 .

M-Estimation and Spatial Quantiles

V. Koltchinskii, 1996 .

Komlos-Major-Tusnady approximation for the general empirical process and Haar expansions of classes of functions

Vladimir Koltchinskii, V. Koltchinskii, 1994 .

Bahadur-Kiefer Approximation for Spatial Quantiles

V. Koltchinskii, 1994 .

Bounds on margin distributions in learning problems

Vladimir Koltchinskii, V. Koltchinskii, 2003 .

Differentiability of Inverse Operators and Limit Theorems for Inverse Functions

V. Koltchinskii, 1998 .

Asymptotics and Concentration Bounds for Bilinear Forms of Spectral Projectors of Sample Covariance

Vladimir Koltchinskii, Karim Lounici, V. Koltchinskii, 2014, 1408.4643.

Testing for Ellipsoidal Symmetry of a Multivariate Distribution

Vladimir Koltchinskii, Lyudmila Sakhanenko, V. Koltchinskii, 2000 .

Estimation of smooth functionals in high-dimensional models: Bootstrap chains and Gaussian approximation

V. Koltchinskii, 2020, The Annals of Statistics.

Estimation of smooth functionals in normal models: Bias reduction and asymptotic efficiency

Vladimir Koltchinskii, Mayya Zhilova, V. Koltchinskii, 2019, The Annals of Statistics.

Efficient estimation of smooth functionals in Gaussian shift models

Vladimir Koltchinskii, Mayya Zhilova, V. Koltchinskii, 2018, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques.

On inverse problems with unknown operators

Vladimir Koltchinskii, Sam Efromovich, V. Koltchinskii, 2001, IEEE Trans. Inf. Theory.

Sparse Recovery in Large Ensembles of Kernel Machines On-Line Learning and Bandits

Ming Yuan, Vladimir Koltchinskii, V. Koltchinskii, 2008, COLT.

Normal approximation and concentration of spectral projectors of sample covariance

Vladimir Koltchinskii, Karim Lounici, V. Koltchinskii, 2015, 1504.07333.

Rademacher Complexities and Bounding the Excess Risk in Active Learning

Vladimir Koltchinskii, V. Koltchinskii, 2010, J. Mach. Learn. Res..

Spatial Quantiles and their Bahadur-Kiefer Representations

V. Koltchinskii, 1994 .

Integral curves of noisy vector fields and statistical problems in diffusion tensor imaging: nonparametric kernel estimation and hypotheses testing

Vladimir Koltchinskii, Songhe Cai, Lyudmila Sakhanenko, 2007, 0710.3509.

Statistical controller design for the linear benchmark problem

Chaouki T. Abdallah, Peter Dorato, Marco Ariola, 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

New Asymptotic Results in Principal Component Analysis

Vladimir Koltchinskii, Karim Lounici, V. Koltchinskii, 2016, Sankhya A.

Estimation of Smooth Functionals of Location Parameter in Gaussian and Poincaré Random Shift Models

V. Koltchinskii, M. Zhilova, 2021, Sankhya A.

Estimation of smooth functionals of covariance operators: jackknife bias reduction and bounds in terms of effective rank

V. Koltchinskii, 2022, 2205.10280.

Scientific Committee

V. Koltchinskii, S. Mendelson, V. Milman, 2004 .

Exponential Convergence Rates in Classification

Vladimir Koltchinskii, Olexandra Beznosova, V. Koltchinskii, 2005, COLT.