Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

24 April 2018

Papers citing "Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization"

13 / 13 papers shown

Title
Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization Dmitry Kovalev 52 0 0 16 Mar 2025
From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization M. Pedramfar Vaneet Aggarwal 23 3 0 27 Apr 2024
A Unified Approach for Maximizing Continuous DR-submodular Functions M. Pedramfar Christopher J. Quinn Vaneet Aggarwal 6 7 0 26 May 2023
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features Aleksandr Beznosikov David Dobre Gauthier Gidel 25 5 0 23 Apr 2023
Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization Gideon Dresdner Maria-Luiza Vladarean Olivier Fercoq Francesco Locatello V. Cevher A. Yurtsever 18 1 0 26 Feb 2022
Minimax Optimization: The Case of Convex-Submodular Arman Adibi Aryan Mokhtari Hamed Hassani 10 7 0 01 Nov 2021
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings Jan Macdonald Mathieu Besançon S. Pokutta 27 11 0 15 Oct 2021
Heavy Ball Momentum for Conditional Gradient Bingcong Li A. Sadeghi G. Giannakis 23 5 0 08 Oct 2021
A Boosting Approach to Reinforcement Learning Nataly Brukhim Elad Hazan Karan Singh 35 13 0 22 Aug 2021
Online Boosting with Bandit Feedback Nataly Brukhim Elad Hazan 21 10 0 23 Jul 2020
Submodular Meta-Learning Arman Adibi Aryan Mokhtari Hamed Hassani CLL 21 5 0 11 Jul 2020
Continuous Submodular Maximization: Beyond DR-Submodularity Moran Feldman Amin Karbasi 14 5 0 21 Jun 2020
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free Mingrui Zhang Lin Chen Aryan Mokhtari Hamed Hassani Amin Karbasi 16 8 0 17 Feb 2019