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Mathematics

D-Index
33
Citations
6869
World Ranking
3000
National Ranking
1211

Overview

Vladimir Koltchinskii is affiliated with the Georgia Institute of Technology in the United States. Their research primarily spans the field of Mathematics, with a focus on Statistics and Probability as the main subfield, alongside contributions in Artificial Intelligence, Finance, Applied Mathematics, and Mathematical Physics.

The scientist has produced work concentrated around several core topics, including Statistical Methods and Inference, Bayesian Methods and Mixture Models, Markov Chains and Monte Carlo Methods, Stochastic Processes and Financial Applications, Mathematical Analysis and Transform Methods, Random Matrices and Applications, and Statistical Methods and Bayesian Inference.

Recent publications by Vladimir Koltchinskii include:

  • Estimation of smooth functionals in high-dimensional models: Bootstrap chains and Gaussian approximation, 2022, The Annals of Statistics
  • Estimation of smooth functionals in normal models: Bias reduction and asymptotic efficiency, 2021, The Annals of Statistics
  • Asymptotically efficient estimation of smooth functionals of covariance operators, 2020, Journal of the European Mathematical Society
  • Estimation of Smooth Functionals of Location Parameter in Gaussian and Poincaré Random Shift Models, 2021, Sankhya A
  • Efficient estimation of smooth functionals in Gaussian shift models, 2021, Annales de l'Institut Henri Poincaré Probabilités et Statistiques

The venues in which the scientist frequently publishes include:

  • arXiv (Cornell University)
  • The Annals of Statistics
  • Annales de l'Institut Henri Poincaré Probabilités et Statistiques
  • Journal of the European Mathematical Society
  • Sankhya A

Collaborations formed a notable part of Koltchinskii's work, with frequent coauthors comprising Mayya Zhilova, Martin Wahl, Matthias Löffler, Richard Nickl, and Minghao Li. These collaborations reflect involvement in interdisciplinary dialogues within statistical and mathematical research.

Best Publications

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

    Vladimir Koltchinskii;Karim Lounici;Alexandre B. Tsybakov

  • Empirical margin distributions and bounding the generalization error of combined classifiers

    V. Koltchinskii;D. Panchenko

  • Rademacher penalties and structural risk minimization

    V. Koltchinskii

  • Local Rademacher complexities and oracle inequalities in risk minimization

    Vladimir Koltchinskii

  • Oracle inequalities in empirical risk minimization and sparse recovery problems

    Vladimir Koltchinskii;École d'été de probabilités de Saint-Flour

  • High Dimensional Probability

    Unknown

  • Rademacher Processes and Bounding the Risk of Function Learning

    Unknown

  • Concentration inequalities and moment bounds for sample covariance operators

    Vladimir Koltchinskii;Karim Lounici

  • M-estimation, convexity and quantiles

    Unknown

  • SPARSITY IN MULTIPLE KERNEL LEARNING

    Vladimir Koltchinskii;Ming Yuan

  • Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: École d'Été de Probabilités de Saint-Flour XXXVIII-2008

    Vladimir Koltchinskii;Ecole d'été de probabilités de Saint-Flour

  • Bounding the Smallest Singular Value of a Random Matrix Without Concentration

    Vladimir Koltchinskii;Shahar Mendelson

  • Sparsity in penalized empirical risk minimization

    Vladimir Koltchinskii

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

    Evarist Giné;Vladimir Koltchinskii

  • Random matrix approximation of spectra of integral operators

    Vladimir Koltchinskii;Evarist Giné

  • The Dantzig selector and sparsity oracle inequalities

    Vladimir Koltchinskii

  • Concentration inequalities and asymptotic results for ratio type empirical processes

    Evarist Giné;Vladimir Koltchinskii

  • Von Neumann entropy penalization and low-rank matrix estimation

    Vladimir Koltchinskii

  • Rademacher Complexities and Bounding the Excess Risk in Active Learning

    Vladimir Koltchinskii

  • Asymptotics and concentration bounds for bilinear forms of spectral projectors of sample covariance

    Vladimir Koltchinskii;Karim Lounici

  • Normal approximation and concentration of spectral projectors of sample covariance

    Vladimir Koltchinskii;Karim Lounici

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

    Vladimir Koltchinskii;Alexandre B. Tsybakov;Karim Lounici

  • Complexities of convex combinations and bounding the generalization error in classification

    Vladimir Koltchinskii;Dmitry Panchenko

  • Sparsity in multiple kernel learning

    Vladimir Koltchinskii;Ming Yuan

  • Rejoinder: Local Rademacher complexities and oracle inequalities in risk minimization

    Vladimir Koltchinskii

Frequent Co-Authors

Alexandre B. Tsybakov
Alexandre B. Tsybakov École Nationale de la Statistique et de l'Administration Économique
Ming Yuan
Ming Yuan Columbia University
Dominique Picard
Dominique Picard Université Paris Cité
Jon A. Wellner
Jon A. Wellner University of Washington
Shahar Mendelson
Shahar Mendelson Texas A&M University
Bin Yu
Bin Yu University of California, Berkeley
Olivier Bousquet
Olivier Bousquet Google (United States)
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
Gerard Kerkyacharian
Gerard Kerkyacharian Université Paris Cité

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