Incremental Without Replacement Sampling in Nonconvex Optimization

Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning

(2020) Jérôme Bolte et al.   MATHEMATICAL PROGRAMMING

Distributed stochastic gradient tracking methods

(2020) Shi Pu et al.   MATHEMATICAL PROGRAMMING

Stochastic Subgradient Method Converges on Tame Functions

(2019) Damek Davis et al.   FOUNDATIONS OF COMPUTATIONAL MATHEMATICS

Why random reshuffling beats stochastic gradient descent

(2019) M. Gürbüzbalaban et al.   MATHEMATICAL PROGRAMMING

Optimization Methods for Large-Scale Machine Learning

(2018) Léon Bottou et al.   SIAM REVIEW

Stochastic Learning Under Random Reshuffling With Constant Step-Sizes

(2018) Bicheng Ying et al.   IEEE TRANSACTIONS ON SIGNAL PROCESSING

Communication-efficient algorithms for decentralized and stochastic optimization

(2018) Guanghui Lan et al.   MATHEMATICAL PROGRAMMING

Perturbed Iterate Analysis for Asynchronous Stochastic Optimization

(2017) Horia Mania et al.   SIAM JOURNAL ON OPTIMIZATION

On the Convergence Rate of Incremental Aggregated Gradient Algorithms

(2017) M. Gürbüzbalaban et al.   SIAM JOURNAL ON OPTIMIZATION

Deep learning

(2015) Yann LeCun et al.   NATURE

Proximal alternating linearized minimization for nonconvex and nonsmooth problems

(2013) Jérôme Bolte et al.   MATHEMATICAL PROGRAMMING

Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming

(2013) Saeed Ghadimi et al.   SIAM JOURNAL ON OPTIMIZATION