World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
40
Citations
8994
World Ranking
2009
National Ranking
849

Engineering and Technology

D-Index
40
Citations
8997
World Ranking
7184
National Ranking
1958

Overview

Katya Scheinberg is affiliated with Cornell University in the United States and has contributed extensively to research in computer science and engineering. Their work mainly focuses on areas such as artificial intelligence, computational mechanics, management science and operations research, statistics and probability, and numerical analysis.

Key research topics explored in their publications include:

  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Advanced Bandit Algorithms Research
  • Advanced Optimization Algorithms Research
  • Markov Chains and Monte Carlo Methods
  • Neural Networks and Applications
  • Machine Learning and Algorithms

Katya Scheinberg has co-authored papers with several frequent collaborators, such as:

  • Lam M. Nguyen
  • Miaolan Xie
  • Albert S. Berahas
  • Liyuan Cao
  • Billy Jin

The scientist's research has appeared in multiple journal and conference venues, including:

  • arXiv (Cornell University)
  • SIAM Journal on Optimization
  • Mathematical Programming
  • Journal of Global Optimization
  • Foundations of Computational Mathematics

Notable recent publications by Katya Scheinberg include:

  • A Stochastic Line Search Method with Expected Complexity Analysis, 2020, SIAM Journal on Optimization
  • Optimal decision trees for categorical data via integer programming, 2021, Journal of Global Optimization
  • A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization, 2021, Foundations of Computational Mathematics
  • Feature engineering and forecasting via derivative-free optimization and ensemble of sequence-to-sequence networks with applications in renewable energy, 2020, Energy
  • Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms, 2020, IEEE Signal Processing Magazine

Best Publications

  • Introduction to derivative-free optimization

    Andrew R. Conn;Katya Scheinberg;Luis N. Vicente

  • Efficient svm training using low-rank kernel representations

    Shai Fine;Katya Scheinberg

  • SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

    Lam M. Nguyen;Jie Liu;Katya Scheinberg;Martin Takáč

  • Recent progress in unconstrained nonlinear optimization without derivatives

    A. R. Conn;K. Scheinberg;Ph. L. Toint

  • Fast alternating linearization methods for minimizing the sum of two convex functions

    Donald Goldfarb;Shiqian Ma;Katya Scheinberg

  • Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points

    Andrew R. Conn;Katya Scheinberg;Luís N. Vicente

  • On the convergence of derivative-free methods for unconstrained optimization

    Andy Conn;Katya Scheinberg;Philippe Toint

  • Efficient block-coordinate descent algorithms for the Group Lasso

    Zhiwei Qin;Katya Scheinberg;Donald Goldfarb

  • Sparse Inverse Covariance Selection via Alternating Linearization Methods

    Katya Scheinberg;Shiqian Ma;Donald Goldfarb

  • Geometry of interpolation sets in derivative free optimization

    A. R. Conn;K. Scheinberg;Luís N. Vicente

  • Stochastic optimization using a trust-region method and random models

    Ruobing Chen;Matt Menickelly;Katya Scheinberg

  • A derivative free optimization algorithm in practice

    A. Conn;K. Scheinberg;Ph. Toint

  • IBM Research TRECVID-2006 Video Retrieval System

    Murray Campbell;Alexander Haubold;Shahram Ebadollahi;Dhiraj Joshi

  • Global convergence rate analysis of unconstrained optimization methods based on probabilistic models

    Coralia Cartis;Katya Scheinberg

  • SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

    Lam M. Nguyen;Phuong Ha Nguyen;Marten van Dijk;Peter Richtárik

  • Convergence of Trust-Region Methods Based on Probabilistic Models

    Afonso S. Bandeira;Katya Scheinberg;Luís Nunes Vicente

  • A Derivative-Free Algorithm for Least-Squares Minimization

    Hongchao Zhang;Andrew R. Conn;Katya Scheinberg

  • A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization

    Albert S. Berahas;Liyuan Cao;Krzysztof Choromanski;Katya Scheinberg

  • Interior Point Trajectories in Semidefinite Programming

    D. Goldfarb;K. Scheinberg

  • Least-squares approach to risk parity in portfolio selection

    Xi Bai;Katya Scheinberg;Reha Tutuncu

  • Geometry of sample sets in derivative-free optimization: polynomial regression and underdetermined interpolation

    Andrew R. Conn;Katya Scheinberg;Luís Nunes Vicente

Frequent Co-Authors

Luís Nunes Vicente
Luís Nunes Vicente Lehigh University
Donald Goldfarb
Donald Goldfarb Columbia University
Irina Rish
Irina Rish University of Montreal
Marten van Dijk
Marten van Dijk University of Connecticut
Shiqian Ma
Shiqian Ma Rice University
Peter Richtárik
Peter Richtárik King Abdullah University of Science and Technology
Jayant R. Kalagnanam
Jayant R. Kalagnanam IBM (United States)
Bhuvana Ramabhadran
Bhuvana Ramabhadran Google (United States)
Dimitri Kanevsky
Dimitri Kanevsky Google (United States)

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