2 citations · 2 across the 7 of their papers we have counts for
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Accelerated and Stable Convergence with Anchored Generalized Optimistic Method
Motahareh Sohrabi, Jianxin You, Simon Lacoste-Julien +2
We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the extragradient method rely on two gradie…
Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases
Ruslan Nazykov, Aleksandr Shestakov, Vladimir Solodkin +3
The Conditional Gradient (or Frank-Wolfe) method is one of the most well-known methods for solving constrained optimization problems appearing in various machine learning tasks. Th…
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
Aleksandr Beznosikov, David Dobre, Gauthier Gidel
The Frank-Wolfe (FW) method is a popular approach for solving optimization problems with structured constraints that arise in machine learning applications. In recent years, stocha…
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova +5
High-probability analysis of stochastic first-order optimization methods under mild assumptions on the noise has been gaining a lot of attention in recent years. Typically, gradien…