Recent Advances in Stochastic Gradient Descent in Deep Learning

Convergence Rates of the Heavy Ball Method for Quasi-strongly Convex Optimization

(2022) J.-F. Aujol et al.   SIAM JOURNAL ON OPTIMIZATION

Combining cross-modal knowledge transfer and semi-supervised learning for speech emotion recognition

(2021) Sheng Zhang et al.   KNOWLEDGE-BASED SYSTEMS

A Survey of Optimization Methods From a Machine Learning Perspective

(2019) Shiliang Sun et al.   IEEE Transactions on Cybernetics

Exact and inexact subsampled Newton methods for optimization

(2018) Raghu Bollapragada et al.   IMA JOURNAL OF NUMERICAL ANALYSIS

Optimization Methods for Large-Scale Machine Learning

(2018) Léon Bottou et al.   SIAM REVIEW

Behavior of accelerated gradient methods near critical points of nonconvex functions

(2018) Michael O’Neill et al.   MATHEMATICAL PROGRAMMING

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

(2018) Fanhua Shang et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization

(2017) Xiao Wang et al.   SIAM JOURNAL ON OPTIMIZATION

A Stochastic Quasi-Newton Method for Large-Scale Optimization

(2016) R. H. Byrd et al.   SIAM JOURNAL ON OPTIMIZATION

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

(2016) Geert Litjens et al.   Scientific Reports

A Generic Proximal Algorithm for Convex Optimization—Application to Total Variation Minimization

(2014)   IEEE SIGNAL PROCESSING LETTERS

RES: Regularized Stochastic BFGS Algorithm

(2014) Aryan Mokhtari et al.   IEEE TRANSACTIONS ON SIGNAL PROCESSING

Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization

(2014) Saeed Ghadimi et al.   MATHEMATICAL PROGRAMMING

iPiano: Inertial Proximal Algorithm for Nonconvex Optimization

(2014) Peter Ochs et al.   SIAM Journal on Imaging Sciences

A Proximal Stochastic Gradient Method with Progressive Variance Reduction

(2014) Lin Xiao et al.   SIAM JOURNAL ON OPTIMIZATION

$L_{1/2}$ Regularization: A Thresholding Representation Theory and a Fast Solver

(2012) Zongben Xu et al.   IEEE Transactions on Neural Networks and Learning Systems

On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning

(2011) Richard H. Byrd et al.   SIAM JOURNAL ON OPTIMIZATION

Nearly unbiased variable selection under minimax concave penalty

(2010) Cun-Hui Zhang   ANNALS OF STATISTICS

Robust Stochastic Approximation Approach to Stochastic Programming

(2009) A. Nemirovski et al.   SIAM JOURNAL ON OPTIMIZATION