Fetching the paper…
Reading the bibliography…
We propose a novel framework for analyzing convergence rates of stochastic optimization algorithms with adaptive step sizes.
Matrix Computations
G. H. Golub and C. F. Van Loan · 1989
Earlier work this paper cites.
Trust Region Methods
A. R. Conn, N. I. M. Gould, and P. T. Toint · 2000
Earlier work this paper cites.
Introductory Lectures on Convex Optimization
Yu. Nesterov · 2004
Earlier work this paper cites.
Numerical Optimization
J. Nocedal and S. J. Wright · 2006
Earlier work this paper cites.
Trust region newton method for large-scale logistic regression
Chih jen Lin, Ruby C. Weng, S. Sathiya Keerthi, and Alexander Smola · 2007
Earlier work this paper cites.
Introduction to Derivative-Free Optimization
A.R. Conn, K. Scheinberg, and L.N. Vicente · 2009
Earlier work this paper cites.
Renewal, recurrence and regeneration, 2010
Gerold Alsmeyer · 2010
Earlier work this paper cites.
Derivative-free optimization of expensive functions with computational error using weighted regression
S. Billups, J. Larsson, and P Graf · 2011
Earlier work this paper cites.
Sample size selection in optimization methods for machine learning
R. Byrd, G. M. Chin, J. Nocedal, and Y. Wu · 2012
Earlier work this paper cites.
Hybrid deterministic-stochastic methods for data fitting
M Friedlander and M. Schmidt · 2012
Earlier work this paper cites.
Convergence of trust-region methods based on probabilistic models
A. Bandeira, K. Scheinberg, and L.N. Vicente · 2013
Cited alongside, same era.
Stochastic trust-region response-surface method (strong) - a new response-surface framework for simulation optimization
K.H. Chang, M.K. Li, and H. Wan · 2013
Cited alongside, same era.
Stochastic first- and zeroth-order methods for nonconvex stochastic programming
S. Ghadimi and G. Lan · 2013
Cited alongside, same era.
Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
Cited alongside, same era.
Convergence of trust-region methods based on probabilistic models
Afonso S Bandeira, Katya Scheinberg, and Luis Nunes Vicente · 2014
Cited alongside, same era.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Stochastic variance reduction for nonconvex optimization
Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, and Alexander J. Smola · 2016
Closest in time.
Complexity and global rates of trust-region methods based on probabilistic models
S Gratton, C. W. Royer, L. N. Vicente, and Z. Zhang · 2017
Closest in time.
SARAH: A novel method for machine learning problems using stochastic recursive gradient
Lam Nguyen, Jie Liu, Katya Scheinberg, and Martin Takáč · 2017
Closest in time.
Stochastic recursive gradient algorithm for nonconvex optimization
Lam Nguyen, Jie Liu, Katya Scheinberg, and Martin Takáč · 2017
Closest in time.
Stochastic cubic regularization for fast nonconvex optimization
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, and Michael I. Jordan · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
Cited alongside, same era.
SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives
Aaron Defazio, Francis Bach, and Simon Lacoste-Julien · 2014
Cited alongside, same era.
Stochastic derivative-free optimization using a trust region framework
J. Larson and S.C. Billups · 2015
Cited alongside, same era.
Astro-df: A class of adaptive sampling trust-region algorithms for derivative-free simulation optimization
S. Shashaani, F. S. Hashemi, and R. Pasupathy · 2015
Cited alongside, same era.
An introduction to matrix concentration inequalities
Joel A. Tropp · 2015
Cited alongside, same era.
Newton-type methods for non-convex optimization under inexact hessian information
Peng Xu, Farbod Roosta-Khorasani, and Michael W. Mahoney · 2017
Closest in time.
Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
C. Cartis and K. Scheinberg · 2018
Closest in time.
Stochastic optimization using a trust-region method and random models
R. Chen, M. Menickelly, and K. Scheinberg · 2018
Closest in time.
A stochastic line search method with expected complexity analysis
Courtney Paquette and Katya Scheinberg · 2018
Closest in time.