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Optimization lies at the heart of machine learning and signal processing.
A Stochastic Approximation Method
H. Robbins and S. Monro · 1951
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Cubic regularization of newton method and its global performance
Yu. Nesterov and B. T. Polyak · 2006
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Numerical Optimization
J. Nocedal and S. J. Wright · 2006
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Adaptive cubic regularisation methods for unconstrained optimization. Part I: Motivation, convergence and numerical results
C Cartis, N. I. M. Gould, and Ph. L. Toint · 2011
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Adaptive cubic regularisation methods for unconstrained optimization. Part II: Worst-case function- and derivative-evaluation complexity
C. Cartis, N. I. M. Gould, and Ph. L. Toint · 2011
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Optimization for Machine Learning
S. Sra, S. Nowozin, and S. J. Wright · 2011
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Sample size selection in optimization methods for machine learning
R. Byrd, G. M. Chin, J. Nocedal, and Y. Wu · 2012
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Hybrid deterministic-stochastic methods for data fitting
M. Friedlander and M. Schmidt · 2012
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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
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Accelerating Stochastic Gradient Descent Using Predictive Variance Reduction
R. Johnson and T. Zhang · 2013
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SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
A. Defazio, F. Bach, and S. Lacoste-Julien · 2014
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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A trust region algorithm with a worst-case iteration complexity of 𝒪 ( ϵ − 3 / 2 ) \mathcal{O}(\epsilon^{-3/2}) for nonconvex optimization
F. E. Curtis, D. P. Robinson, and M. Samadi · 2017
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Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning
F. E. Curtis and K. Scheinberg · 2017
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Probabilistic line searches for stochastic optimization
M. Mahsereci and P. Hennig · 2017
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SARAH: A novel method for machine learning problems using stochastic recursive gradient
Stochastic optimization using a trust-region method and random models
R. Chen, M. Menickelly, and K. Scheinberg · 2018
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Foundations of Machine Learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2018
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A stochastic line search method with expected complexity analysis
C. Paquette and K. Scheinberg · 2018
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On sampling rates in simulation-based recursions
Raghu. Pasupathy, Peter. Glynn, Soumyadip. Ghosh, and Fatemeh S. Hashemi · 2018
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Stochastic cubic regularization for fast nonconvex optimization
N. Tripuraneni, M. Stern, C. Jin, J. Regier, and M. I. Jordan · 2018
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The importance of better models in stochastic optimization
H. Asi and J. C. Duchi · 2019
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L. M. Nguyen, J. Liu, K. Scheinberg, and M. Takáč · 2017
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Minimizing finite sums with the stochastic average gradient
M. Schmidt, N. Le Roux, and F. Bach · 2017
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Adaptive sampling strategies for stochastic optimization
R. Bollapragada, R. Byrd, and J. Nocedal · 2018
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Optimization Methods for Large-Scale Machine Learning
L. Bottou, F. E. Curtis, and J. Nocedal · 2018
Cited alongside, same era.
Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
C. Cartis and K. Scheinberg · 2018
Cited alongside, same era.
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Convergence rate analysis of a stochastic trust-region method via supermartingales
J. Blanchet, C. Cartis, M. Menickelly, and K. Scheinberg · 2019
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A Stochastic Trust Region Algorithm Based on Careful Step Normalization
F. E. Curtis, K. Scheinberg, and R. Shi · 2019
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Painless stochastic gradient: Interpolation, line-search, and convergence rates
S. Vaswani, A. Mishkin, I. H. Laradji, M. W. Schmidt, G. Gidel, and S. Lacoste-Julien · 2019
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Newton-type methods for non-convex optimization under inexact hessian information
P. Xu, F. Roosta, and M. W. Mahoney · 2019
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