Katya Scheinberg

Georgia Institute of Technology — United States

Engineering · Physical Sciences

37h-index6.7kcitations130works1.92yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Sparse and Compressive Sensing Techniques(62), Stochastic Gradient Optimization Techniques(49), Advanced Optimization Algorithms Research(47), Optimization and Variational Analysis(12), Machine Learning and Algorithms(12)

Publications130 total

Editor’s Comments on the 50th Anniversary of <i>Mathematics of Operations Research</i>
Mathematics of Operations Research·2026
ProxSTORM -- A Stochastic Trust-Region Algorithm for Nonsmooth Optimization
arXiv (Cornell University)·2025Open Access
Novel and Efficient Approximations for Zero-One and Ranking Losses of Linear Classifiers
Vietnam Journal of Mathematics·2025Open Access
Stochastic Gradients: Optimization, Simulation, Randomization, and Sensitivity Analysis
IISE Transactions·2025· 1 cited
Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization
Journal of Optimization Theory and Applications·2025
ProxSTORM - A Stochastic Trust Region Method for Nonsmooth Optimization
2024Open Access
High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles
SIAM Journal on Optimization·2024· 8 cited
Sample complexity analysis for adaptive optimization algorithms with stochastic oracles
Mathematical Programming·2024· 8 cited
Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization
arXiv (Cornell University)·2024Open Access
Stochastic Adaptive Regularization Method with Cubics: a High Probability Complexity Bound
2023· 5 cited
First- and Second-Order Stochastic Adaptive Regularization with Cubics: High Probability Iteration and Sample Complexity
arXiv (Cornell University)·2023· 1 citedOpen Access
First- and second-order high probability complexity bounds for trust-region methods with noisy oracles
Mathematical Programming·2023· 21 cited
Sample Complexity Analysis for Adaptive Optimization Algorithms with Stochastic Oracles
arXiv (Cornell University)·2023Open Access
Finite Difference Gradient Approximation: To Randomize or Not?
INFORMS journal on computing·2022· 26 cited
Finding Optimal Policy for Queueing Models: New Parameterization
arXiv (Cornell University)·2022· 2 citedOpen Access
First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oracles
arXiv (Cornell University)·2022· 1 citedOpen Access
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
arXiv (Cornell University)·2022· 2 citedOpen Access
High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles
arXiv (Cornell University)·2021· 2 citedOpen Access
High Probability Complexity Bounds for Line Search Based on Stochastic Oracles
arXiv (Cornell University)·2021· 2 citedOpen Access
A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization
Foundations of Computational Mathematics·2021· 39 citedOpen Access
page 1 of 7Next →

Frequent Co-authors

Donald Goldfarb(10), Andrew R. Conn(7), L. N. Vicente(7), Lam M. Nguyen(5), Martin Takáč(5), Philippe L. Toint(4), Matt Menickelly(4), Shiqian Ma(3), Coralia Cartis(3), Xi Bai(3), Frank E. Curtis(3), Afonso S. Bandeira(2), José Blanchet(2), Reha Tütüncü(2), Courtney Paquette(2), Jie Liu(2), Marten van Dijk(2), Peter Richtárik(2), Shai Fine(1), Jie Liu(1)