Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization

6 June 2021

Wenzhi Gao

Papers citing "Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization"

23 / 23 papers shown

Title
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization Karan N. Chadha Gary Cheng John C. Duchi 74 16 0 07 Jan 2021
Almost sure convergence rates for Stochastic Gradient Descent and Stochastic Heavy Ball Othmane Sebbouh Robert Mansel Gower Aaron Defazio 41 22 0 14 Jun 2020
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses Raef Bassily Vitaly Feldman Cristóbal Guzmán Kunal Talwar MLT 47 194 0 12 Jun 2020
Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization Vien V. Mai M. Johansson 54 55 0 13 Feb 2020
Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions J.N. Zhang Hongzhou Lin Stefanie Jegelka Ali Jadbabaie S. Sra 42 44 0 10 Feb 2020
Understanding the Role of Momentum in Stochastic Gradient Methods Igor Gitman Hunter Lang Pengchuan Zhang Lin Xiao 50 95 0 30 Oct 2019
Generalized Momentum-Based Methods: A Hamiltonian Perspective Jelena Diakonikolas Michael I. Jordan 55 57 0 02 Jun 2019
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence Vasileios Charisopoulos Yudong Chen Damek Davis Mateo Díaz Lijun Ding Dmitriy Drusvyatskiy 56 85 0 22 Apr 2019
The importance of better models in stochastic optimization Hilal Asi John C. Duchi 38 73 0 20 Mar 2019
Deep Frank-Wolfe For Neural Network Optimization Leonard Berrada Andrew Zisserman M. P. Kumar ODL 30 40 0 19 Nov 2018
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity Hilal Asi John C. Duchi 120 124 0 12 Oct 2018
A Unified Analysis of Stochastic Momentum Methods for Deep Learning Yan Yan Tianbao Yang Zhe Li Qihang Lin Yi Yang 33 119 0 30 Aug 2018
Stochastic model-based minimization of weakly convex functions Damek Davis Dmitriy Drusvyatskiy 72 376 0 17 Mar 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 62 201 0 27 Dec 2017
Proximal Backpropagation Thomas Frerix Thomas Möllenhoff Michael Möller Daniel Cremers 46 31 0 14 Jun 2017
Practical Gauss-Newton Optimisation for Deep Learning Aleksandar Botev H. Ritter David Barber ODL 49 231 0 12 Jun 2017
Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval John C. Duchi Feng Ruan 47 166 0 05 May 2017
Stochastic Methods for Composite and Weakly Convex Optimization Problems John C. Duchi Feng Ruan 32 127 0 24 Mar 2017
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox Jialei Wang Weiran Wang Nathan Srebro 82 54 0 21 Feb 2017
Train faster, generalize better: Stability of stochastic gradient descent Moritz Hardt Benjamin Recht Y. Singer 111 1,238 0 03 Sep 2015
Distributed Mini-Batch SDCA Martin Takáč Peter Richtárik Nathan Srebro 64 50 0 29 Jul 2015
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming Saeed Ghadimi Guanghui Lan ODL 120 1,548 0 22 Sep 2013
Optimal Distributed Online Prediction using Mini-Batches O. Dekel Ran Gilad-Bachrach Ohad Shamir Lin Xiao 257 685 0 07 Dec 2010