Katya Scheinberg

According to our database, Katya Scheinberg authored at least 65 papers between 1996 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Stochastic Adaptive Regularization Method with Cubics: a High Probability Complexity Bound.

Katya Scheinberg, Miaolan Xie

Proceedings of the Winter Simulation Conference, 2023

2022

Finite Difference Gradient Approximation: To Randomize or Not?

Katya Scheinberg

INFORMS J. Comput., 2022

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

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

Found. Comput. Math., 2022

Finding Optimal Policy for Queueing Models: New Parameterization.

Trang H. Tran, Lam M. Nguyen, Katya Scheinberg

CoRR, 2022

Nesterov Accelerated Shuffling Gradient Method for Convex Optimization.

Trang H. Tran, Katya Scheinberg, Lam M. Nguyen

Proceedings of the International Conference on Machine Learning, 2022

2021

Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise.

Albert S. Berahas, Liyuan Cao, Katya Scheinberg

SIAM J. Optim., 2021

Inexact SARAH algorithm for stochastic optimization.

Lam M. Nguyen, Katya Scheinberg, Martin Takác

Optim. Methods Softw., 2021

Optimal decision trees for categorical data via integer programming.

Oktay Günlük, Jayant Kalagnanam, Minhan Li, Matt Menickelly, Katya Scheinberg

J. Glob. Optim., 2021

High Probability Complexity Bounds for Line Search Based on Stochastic Oracles.

Billy Jin, Katya Scheinberg, Miaolan Xie

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms.

Frank E. Curtis, Katya Scheinberg

IEEE Signal Process. Mag., 2020

A Stochastic Line Search Method with Expected Complexity Analysis.

Courtney Paquette, Katya Scheinberg

SIAM J. Optim., 2020

Adaptive Stochastic Optimization.

Frank E. Curtis, Katya Scheinberg

CoRR, 2020

2019

A Stochastic Trust Region Algorithm Based on Careful Step Normalization.

Frank E. Curtis, Katya Scheinberg, Rui Shi

INFORMS J. Optim., July, 2019

Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales.

Jose H. Blanchet, Coralia Cartis, Matt Menickelly, Katya Scheinberg

INFORMS J. Optim., April, 2019

New Convergence Aspects of Stochastic Gradient Algorithms.

Lam M. Nguyen, Phuong Ha Nguyen, Peter Richtárik, Katya Scheinberg, Martin Takác, Marten van Dijk

J. Mach. Learn. Res., 2019

A Novel Smoothed Loss and Penalty Function for Noncrossing Composite Quantile Estimation via Deep Neural Networks.

Kostas Hatalis, Alberto J. Lamadrid, Katya Scheinberg, Shalinee Kishore

CoRR, 2019

Feature Engineering and Forecasting via Integration of Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks: Renewable Energy Case Studies.

Mohammad Pirhooshyaran, Lawrence V. Snyder, Katya Scheinberg

CoRR, 2019

Novel and Efficient Approximations for Zero-One Loss of Linear Classifiers.

Hiva Ghanbari, Minhan Li, Katya Scheinberg

CoRR, 2019

2018

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

Ruobing Chen, Matt Menickelly, Katya Scheinberg

Math. Program., 2018

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

Coralia Cartis, Katya Scheinberg

Math. Program., 2018

Directly and Efficiently Optimizing Prediction Error and AUC of Linear Classifiers.

Hiva Ghanbari, Katya Scheinberg

CoRR, 2018

When Does Stochastic Gradient Algorithm Work Well?

Lam M. Nguyen, Nam H. Nguyen, Dzung T. Phan, Jayant R. Kalagnanam, Katya Scheinberg

CoRR, 2018

Proximal quasi-Newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates.

Hiva Ghanbari, Katya Scheinberg

Comput. Optim. Appl., 2018

SGD and Hogwild! Convergence Without the Bounded Gradients Assumption.

Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takác

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

On the construction of quadratic models for derivative-free trust-region algorithms.

Adriano Verdério, Elizabeth W. Karas, Lucas G. Pedroso, Katya Scheinberg

EURO J. Comput. Optim., 2017

Stochastic Recursive Gradient Algorithm for Nonconvex Optimization.

Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác

CoRR, 2017

Black-Box Optimization in Machine Learning with Trust Region Based Derivative Free Algorithm.

Hiva Ghanbari, Katya Scheinberg

CoRR, 2017

Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning.

Frank E. Curtis, Katya Scheinberg

CoRR, 2017

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

Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takác

Proceedings of the 34th International Conference on Machine Learning, 2017

A Novel l0-Constrained Gaussian Graphical Model for Anomaly Localization.

Dzung T. Phan, Tsuyoshi Idé, Jayant Kalagnanam, Matt Menickelly, Katya Scheinberg

Proceedings of the 2017 IEEE International Conference on Data Mining Workshops, 2017

An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power.

Kostas Hatalis, Shalinee Kishore, Katya Scheinberg, Alberto J. Lamadrid

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Practical inexact proximal quasi-Newton method with global complexity analysis.

Katya Scheinberg, Xiaocheng Tang

Math. Program., 2016

Optimal Generalized Decision Trees via Integer Programming.

Matt Menickelly, Oktay Günlük, Jayant Kalagnanam, Katya Scheinberg

CoRR, 2016

Proximal Quasi-Newton Methods for Convex Optimization.

Hiva Ghanbari, Katya Scheinberg

CoRR, 2016

2015

A scalable solution for group feature selection.

Priya Govindan, Ruobing Chen, Katya Scheinberg, Soundararajan Srinivasan

Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015

Superposition of protein structures using electrostatic isopotentials.

Ziyi Guo, Katya Scheinberg, Juliana Hong, Brian Yuan Chen

Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine, 2015

2014

Convergence of Trust-Region Methods Based on Probabilistic Models.

Afonso S. Bandeira, Katya Scheinberg, Luís Nunes Vicente

SIAM J. Optim., 2014

Fast First-Order Methods for Composite Convex Optimization with Backtracking.

Katya Scheinberg, Donald Goldfarb, Xi Bai

Found. Comput. Math., 2014

2013

Efficient block-coordinate descent algorithms for the Group Lasso.

Zhiwei (Tony) Qin, Katya Scheinberg, Donald Goldfarb

Math. Program. Comput., 2013

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

Donald Goldfarb, Shiqian Ma, Katya Scheinberg

Math. Program., 2013

On partial sparse recovery

Afonso S. Bandeira, Katya Scheinberg, Luís Nunes Vicente

CoRR, 2013

Efficiently Using Second Order Information in Large l1 Regularization Problems

Xiaocheng Tang, Katya Scheinberg

CoRR, 2013

Complexity of Inexact Proximal Newton methods.

Katya Scheinberg, Xiaocheng Tang

CoRR, 2013

2012

Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization.

Afonso S. Bandeira, Katya Scheinberg, Luís Nunes Vicente

Math. Program., 2012

Aligning ligand binding cavities by optimizing superposed volume.

Ruobing Chen, Katya Scheinberg, Brian Yuan Chen

Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine, 2012

2010

A Derivative-Free Algorithm for Least-Squares Minimization.

Hongchao Zhang, Andrew R. Conn, Katya Scheinberg

SIAM J. Optim., 2010

Self-Correcting Geometry in Model-Based Algorithms for Derivative-Free Unconstrained Optimization.

Katya Scheinberg, Philippe L. Toint

SIAM J. Optim., 2010

Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach.

Katya Scheinberg, Irina Rish

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Sparse Inverse Covariance Selection via Alternating Linearization Methods.

Katya Scheinberg, Shiqian Ma, Donald Goldfarb

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Sparse Markov net learning with priors on regularization parameters.

Katya Scheinberg, Irina Rish, Narges Bani Asadi

Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2010

2009

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

Andrew R. Conn, Katya Scheinberg, Luís Nunes Vicente

SIAM J. Optim., 2009

Map approach to learning sparse Gaussian Markov networks.

Narges Bani Asadi, Irina Rish, Katya Scheinberg, Dimitri Kanevsky, Bhuvana Ramabhadran

Proceedings of the IEEE International Conference on Acoustics, 2009

Introduction to Derivative-Free Optimization.

Andrew R. Conn, Katya Scheinberg, Luís Nunes Vicente

MPS-SIAM series on optimization 8, SIAM, ISBN: 978-0-89871-668-9, 2009

2008

PREFACESpecial section on mathematical programming in data mining and machine learning.

Katya Scheinberg, Jiming Peng

Optim. Methods Softw., 2008

Geometry of interpolation sets in derivative free optimization.

Andrew R. Conn, Katya Scheinberg, Luís Nunes Vicente

Math. Program., 2008

2006

An Efficient Implementation of an Active Set Method for SVMs.

Katya Scheinberg

J. Mach. Learn. Res., 2006

IBM Research TRECVID-2006 Video Retrieval System.

Murray Campbell, Alexander Haubold, Shahram Ebadollahi, Dhiraj Joshi, Milind R. Naphade, Apostol Natsev, Joachim Seidl, John R. Smith, Katya Scheinberg, Jelena Tesic, Lexing Xie

Proceedings of the 2006 TREC Video Retrieval Evaluation, 2006

2005

Product-form Cholesky factorization in interior point methods for second-order cone programming.

Donald Goldfarb, Katya Scheinberg

Math. Program., 2005

2004

A product-form Cholesky factorization method for handling dense columns in interior point methods for linear programming.

Donald Goldfarb, Katya Scheinberg

Math. Program., 2004

2001

Efficient SVM Training Using Low-Rank Kernel Representations.

Shai Fine, Katya Scheinberg

J. Mach. Learn. Res., 2001

Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM.

Shai Fine, Katya Scheinberg

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

1999

A Modified Barrier-Augmented Lagrangian Method for Constrained Minimization.

Donald Goldfarb, R. Polyak, Katya Scheinberg, I. Yuzefovich

Comput. Optim. Appl., 1999

1998

Interior Point Trajectories in Semidefinite Programming.

Donald Goldfarb, Katya Scheinberg

SIAM J. Optim., 1998

1997

Recent progress in unconstrained nonlinear optimization without derivatives.

Andrew R. Conn, Katya Scheinberg, Philippe L. Toint

Math. Program., 1997

1996

Extension of Karmarkar's algorithm onto convex quadratically constrained quadratic problems.

Arkadii Nemirovskii, Katya Scheinberg

Math. Program., 1996