Katya Scheinberg
Katya Scheinberg
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Introduction to derivative-free optimization
AR Conn, K Scheinberg, LN Vicente
Society for Industrial and Applied Mathematics, 2009
Efficient SVM training using low-rank kernel representations
S Fine, K Scheinberg
Journal of Machine Learning Research 2 (Dec), 243-264, 2001
SARAH: A novel method for machine learning problems using stochastic recursive gradient
LM Nguyen, J Liu, K Scheinberg, M Takáč
International conference on machine learning, 2613-2621, 2017
Recent progress in unconstrained nonlinear optimization without derivatives
AR Conn, K Scheinberg, PL Toint
Mathematical programming 79, 397-414, 1997
Fast alternating linearization methods for minimizing the sum of two convex functions
D Goldfarb, S Ma, K Scheinberg
Mathematical Programming 141 (1), 349-382, 2013
Global convergence of general derivative-free trust-region algorithms to first-and second-order critical points
AR Conn, K Scheinberg, LN Vicente
SIAM Journal on Optimization 20 (1), 387-415, 2009
On the convergence of derivative-free methods for unconstrained optimization
AR Conn, K Scheinberg, PL Toint
Approximation theory and optimization: tributes to MJD Powell, 83-108, 1997
Sparse inverse covariance selection via alternating linearization methods
K Scheinberg, S Ma, D Goldfarb
Advances in neural information processing systems 23, 2010
Efficient block-coordinate descent algorithms for the group lasso
Z Qin, K Scheinberg, D Goldfarb
Mathematical Programming Computation 5 (2), 143-169, 2013
SGD and Hogwild! convergence without the bounded gradients assumption
L Nguyen, PH Nguyen, M Dijk, P Richtárik, K Scheinberg, M Takác
International Conference on Machine Learning, 3750-3758, 2018
Geometry of interpolation sets in derivative free optimization
AR Conn, K Scheinberg, LN Vicente
Mathematical programming 111, 141-172, 2008
Stochastic optimization using a trust-region method and random models
R Chen, M Menickelly, K Scheinberg
Mathematical Programming 169, 447-487, 2018
A theoretical and empirical comparison of gradient approximations in derivative-free optimization
AS Berahas, L Cao, K Choromanski, K Scheinberg
Foundations of Computational Mathematics 22 (2), 507-560, 2022
Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
C Cartis, K Scheinberg
Mathematical Programming 169, 337-375, 2018
A derivative free optimization algorithm in practice
A Conn, K Scheinberg, P Toint
7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and …, 1998
IBM Research TRECVID-2006 Video Retrieval System.
M Campbell, A Haubold, S Ebadollahi, D Joshi, MR Naphade, A Natsev, ...
TRECVID, 175-182, 2006
Convergence rate analysis of a stochastic trust-region method via supermartingales
J Blanchet, C Cartis, M Menickelly, K Scheinberg
INFORMS journal on optimization 1 (2), 92-119, 2019
A stochastic line search method with expected complexity analysis
C Paquette, K Scheinberg
SIAM Journal on Optimization 30 (1), 349-376, 2020
An efficient implementation of an active set method for SVMs.
K Scheinberg, KP Bennett, E Parrado-Hernández
Journal of Machine Learning Research 7 (10), 2006
Convergence of trust-region methods based on probabilistic models
AS Bandeira, K Scheinberg, LN Vicente
SIAM Journal on Optimization 24 (3), 1238-1264, 2014
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