The importance of better models in stochastic optimization

20 March 2019

Papers citing "The importance of better models in stochastic optimization"

12 / 12 papers shown

Title
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance Dimitris Oikonomou Nicolas Loizou 55 4 0 06 Jun 2024
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule Maor Ivgi Oliver Hinder Y. Carmon ODL 26 57 0 08 Feb 2023
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation Xiao-Tong Yuan P. Li 34 2 0 09 Jan 2023
Learning distributed channel access policies for networked estimation: data-driven optimization in the mean-field regime M. Vasconcelos 24 0 0 10 Dec 2021
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness Vien V. Mai M. Johansson 31 38 0 12 Feb 2021
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms Mahesh Chandra Mukkamala M. Fadili Peter Ochs 22 8 0 24 Dec 2020
Near Instance-Optimality in Differential Privacy Hilal Asi John C. Duchi 26 38 0 16 May 2020
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence Nicolas Loizou Sharan Vaswani I. Laradji Simon Lacoste-Julien 27 181 0 24 Feb 2020
Second-order Conditional Gradient Sliding Alejandro Carderera Sebastian Pokutta 19 12 0 20 Feb 2020
Adaptive Stochastic Optimization Frank E. Curtis K. Scheinberg ODL 16 29 0 18 Jan 2020
Protection Against Reconstruction and Its Applications in Private Federated Learning Abhishek Bhowmick John C. Duchi Julien Freudiger Gaurav Kapoor Ryan M. Rogers FedML 18 357 0 03 Dec 2018
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 271 5,327 0 05 Nov 2016