Katya Scheinberg | Academic Influence

Katya Scheinberg's AcademicInfluence.com Rankings

Katya Scheinberg

Mathematics

#3107

World Rank

#4651

Historical Rank

Operations Research

#46

World Rank

#46

Historical Rank

Applied Mathematics

#245

World Rank

#270

Historical Rank

Measure Theory

#2920

World Rank

#3468

Historical Rank

mathematics Degrees

Download Badge

Mathematics

Katya Scheinberg's Degrees

PhD Operations Research Cornell University
Masters Operations Research Cornell University
Bachelors Mathematics Moscow State University

Similar Degrees You Can Earn

Best Online Bachelor's in Math 2025

Why Is Katya Scheinberg Influential?

(Suggest an Edit or Addition)

According to Wikipedia, Katya Scheinberg is a Russian-American applied mathematician known for her research in continuous optimization and particularly in derivative-free optimization. She works at Cornell University and is a professor in Cornell's School of Operations Research and Information Engineering.

(See a Problem?)

Katya Scheinberg's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Introduction to Derivative-Free Optimization (2009) (882)
Efficient SVM Training Using Low-Rank Kernel Representations (2002) (686)
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient (2017) (423)
Recent progress in unconstrained nonlinear optimization without derivatives (1997) (294)
Fast alternating linearization methods for minimizing the sum of two convex functions (2009) (282)
Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points (2009) (221)
On the convergence of derivative-free methods for unconstrained optimization (1997) (213)
Efficient block-coordinate descent algorithms for the Group Lasso (2013) (205)
Sparse Inverse Covariance Selection via Alternating Linearization Methods (2010) (201)
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption (2018) (157)
Geometry of interpolation sets in derivative free optimization (2007) (146)
A derivative free optimization algorithm in practice (1998) (132)
Stochastic optimization using a trust-region method and random models (2015) (122)
Global convergence rate analysis of unconstrained optimization methods based on probabilistic models (2015) (118)
Convergence of Trust-Region Methods Based on Probabilistic Models (2013) (93)
Interior Point Trajectories in Semidefinite Programming (1998) (91)
Geometry of sample sets in derivative-free optimization: polynomial regression and underdetermined interpolation (2008) (89)
A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization (2019) (86)
On the local convergence of a derivative-free algorithm for least-squares minimization (2010) (85)
An Efficient Implementation of an Active Set Method for SVMs (2006) (83)
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization (2017) (81)
Practical inexact proximal quasi-Newton method with global complexity analysis (2013) (80)
Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales (2016) (79)
Fast First-Order Methods for Composite Convex Optimization with Backtracking (2014) (74)
Least-squares approach to risk parity in portfolio selection (2015) (68)
Self-Correcting Geometry in Model-Based Algorithms for Derivative-Free Unconstrained Optimization (2010) (68)
Block Coordinate Descent Methods for Semidefinite Programming (2012) (64)
Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization (2012) (60)
Duality and Optimality Conditions (2000) (55)
New Convergence Aspects of Stochastic Gradient Algorithms (2018) (53)
A Stochastic Line Search Method with Expected Complexity Analysis (2020) (51)
On parametric semidefinite programming (1999) (51)
Optimal decision trees for categorical data via integer programming (2021) (49)
Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise (2019) (48)
Intensive optimization of masks and sources for 22nm lithography (2009) (47)
Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach (2010) (46)
A Stochastic Line Search Method with Convergence Rate Analysis (2018) (44)
Inexact SARAH algorithm for stochastic optimization (2018) (43)
A Modified Barrier-Augmented Lagrangian Method for Constrained Minimization (1999) (42)
IBM Research Report SINCO - A Greedy Coordinate Ascent Method for Sparse Inverse Covariance Selection Problem (2009) (42)
Convergence Rate Analysis of a Stochastic Trust Region Method for Nonconvex Optimization (2016) (40)
ROW BY ROW METHODS FOR SEMIDEFINITE PROGRAMMING (2009) (36)
Extension of Karmarkar's algorithm onto convex quadratically constrained quadratic problems (1996) (35)
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning (2017) (32)
Optimal Generalized Decision Trees via Integer Programming (2016) (27)
Methodologies and software for derivative-free optimization (2017) (26)
A product-form Cholesky factorization method for handling dense columns in interior point methods for linear programming (2004) (25)
Product-form Cholesky factorization in interior point methods for second-order cone programming (2005) (25)
A Stochastic Trust Region Algorithm Based on Careful Step Normalization (2017) (24)
Proximal quasi-Newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates (2016) (22)
Black-Box Optimization in Machine Learning with Trust Region Based Derivative Free Algorithm (2017) (21)
Smooth Pinball Neural Network for Probabilistic Forecasting of Wind Power (2017) (20)
Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM (2001) (20)
On partial sparse recovery (2013) (20)
Feature Engineering and Forecasting via Integration of Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks: Renewable Energy Case Studies (2019) (15)
Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms (2020) (14)
On Partially Sparse Recovery (2011) (14)
When Does Stochastic Gradient Algorithm Work Well? (2018) (14)
Complexity of Inexact Proximal Newton methods (2013) (13)
On the construction of quadratic models for derivative-free trust-region algorithms (2017) (11)
Alternating direction methods for non convex optimization with applications to second-order least-squares and risk parity portfolio selection (2015) (11)
High Probability Complexity Bounds for Line Search Based on Stochastic Oracles (2021) (11)
Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization (2019) (9)
Solving Structured Convex Quadratic Programs by Interior Point Methods with Application to Support Vector Machines and Portfolio Optimization ∗ (2005) (8)
A DERIVATIVE-FREE ALGORITHM FOR THE LEAST-SQUARE MINIMIZATION (8)
Map approach to learning sparse Gaussian Markov networks (2009) (8)
A Novel Smoothed Loss and Penalty Function for Noncrossing Composite Quantile Estimation via Deep Neural Networks (2019) (7)
Stochastic optimization using a trust-region method and random models (2017) (7)
Numerically stable LDLT factorizations in interior point methods for convex quadratic programming (2008) (6)
Proximal Quasi-Newton Methods for Convex Optimization (2016) (6)
First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oracles (2022) (5)
Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable Energy. (2019) (5)
A Stochastic Trust Region Algorithm (2017) (5)
Finite Difference Gradient Approximation: To Randomize or Not? (2022) (4)
Incas: An incremental active set method for svm (2002) (4)
Error estimates and poisedness in multivariate polynomial interpolation (2003) (4)
High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles (2021) (3)
An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power (2018) (3)
Aligning ligand binding cavities by optimizing superposed volume (2012) (3)
Global convergence rate analysis of unconstrained optimization methods based on probabilistic models (2017) (3)
Sparse Markov net learning with priors on regularization parameters (2010) (3)
Derivative free optimization method (2000) (3)
High Probability Step Size Lower Bound for Adaptive Stochastic Optimization (2021) (2)
A Novel l0-Constrained Gaussian Graphical Model for Anomaly Localization (2017) (2)
Adaptive Stochastic Optimization (2020) (2)
Stochastic Adaptive Regularization Method with Cubics: A High Probability Complexity Bound (2022) (2)
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization (2022) (2)
Fast alternating linearization methods for minimizing the sum of two convex functions (2012) (1)
Structure Prediction and Global Optimization 2 A Celebration of 50 Years of Integer Programming 9 76 A new column for Optima by Alberto Caprara (2008) (1)
Mathematical Programming in Machine Learning and Data Mining (2007) (1)
Superposition of protein structures using electrostatic isopotentials (2015) (1)
Detecting Generic Visual Eventswith Temporal Cues (2006) (1)
13. Review of Constrained and Other Extensions to Derivative-Free Optimization (2009) (1)
Finding Optimal Policy for Queueing Models: New Parameterization (2022) (1)
Rock Physics and Depositional History from Seismic Matching: A model study (2007) (1)
A scalable solution for group feature selection (2015) (1)
Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization (2012) (1)
7. Directional Direct-Search Methods (2009) (1)
12. Review of Surrogate Model Management (2009) (1)
Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers (2019) (1)
Discussion column - Copositive vs. moment hierarchies for stable sets (2012) (1)
Efficiently Using Second Order Information in Large l1 Regularization Problems (2013) (1)
Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers (2018) (1)
Directly and E � ciently Optimizing Prediction Error and AUC of Linear Classifiers Hiva Ghanbari (2018) (0)
PREFACESpecial section on mathematical programming in data mining and machine learning (2008) (0)
5. Underdetermined Interpolating Models (2009) (0)
EVENTDATA.E.E WHEREEe seeisorsey (2017) (0)
Proximal quasi-Newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates (2017) (0)
Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models (2015) (0)
4. Regression Nonlinear Models (2009) (0)
Fast First-Order Methods for Composite Convex Optimization with Backtracking (2014) (0)
REVIEWS AND DESCRIPTIONS OF TABLES AND BOOKS (1998) (0)
PREFACE (2008) (0)
11. Trust-Region Interpolation-Based Methods (2009) (0)
Special section on mathematical programming in data mining and machine learning (2008) (0)
6. Ensuring Well Poisedness and Suitable Derivative-Free Models (2009) (0)
L G ] 7 F eb 2 01 8 Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers Hiva (2018) (0)
Efficient block-coordinate descent algorithms for the Group Lasso (2013) (0)
3. Interpolating Nonlinear Models (2009) (0)
8. Simplicial Direct-Search Methods (2009) (0)
2. Sampling and Linear Models (2009) (0)
OPTIMA Mathematical Programming Society Newsletter 79 (2009) (0)
Are you receiving this by postal mail ? Do you prefer elec (2016) (0)
Sparse Modeling in fMRI Analysis (2009) (0)
Assisted Seismic Matching: Joint Inversion of Seismic, Rock Physics And Basin Modeling (2007) (0)
Spring 2012 Colloquium Temple University Computer and Information Sciences First order methods for the large convex optimization problem arising in sparse inverse covariance selection (2012) (0)
9. Line-Search Methods Based on Simplex Derivatives (2009) (0)
Sample Complexity Analysis for Adaptive Optimization Algorithms with Stochastic Oracles (2023) (0)
A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization (2021) (0)
Practical inexact proximal quasi-Newton method with global complexity analysis (2016) (0)
Chapter 39: Volumetric Alignment of Protein-Binding Cavities (2017) (0)
Proximal Quasi-Newton Methods for Convex Optimization Hiva Ghanbari (2016) (0)
10. Trust-Region Methods Based on Derivative-Free Models (2009) (0)

This paper list is powered by the following services:

Other Resources About Katya Scheinberg

en.wikipedia.org

What Schools Are Affiliated With Katya Scheinberg?

Katya Scheinberg is affiliated with the following schools: