1 citations · 1 across the 8 of their papers we have counts for
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Bregman meets Lévy: Stochastic mirror descent with heavy-tailed noise in continuous and discrete time
Pierre-Louis Cauvin, Panayotis Mertikopoulos
We study the robustness of stochastic mirror descent (SMD) under heavy-tailed noise, focusing on whether the method retains its convergence guarantees when run with infinite-varian…
Explicit Second-Order Min-Max Optimization: Practical Algorithms and Complexity Analysis
Tianyi Lin, Panayotis Mertikopoulos, Michael I. Jordan
We propose and analyze several inexact regularized Newton-type methods for finding a global saddle point of convex-concave unconstrained min-max optimization problems. Compared to…
What is the long-run distribution of stochastic gradient descent? A large deviations analysis
Waïss Azizian, Franck Iutzeler, Jérôme Malick +1
In this paper, we examine the long-run distribution of stochastic gradient descent (SGD) in general, non-convex problems. Specifically, we seek to understand which regions of the p…
The global convergence time of stochastic gradient descent in non-convex landscapes: Sharp estimates via large deviations
Waïss Azizian, Franck Iutzeler, Jérôme Malick +1
In this paper, we examine the time it takes for stochastic gradient descent (SGD) to reach the global minimum of a general, non-convex loss function. We approach this question thro…
Setwise Coordinate Descent for Dual Asynchronous Decentralized Optimization
Marina Costantini, Nikolaos Liakopoulos, Panayotis Mertikopoulos +1
In decentralized optimization over networks, synchronizing the updates of all nodes incurs significant communication overhead. For this reason, much of the recent literature has fo…
The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness
Waïss Azizian, Franck Iutzeler, Jérôme Malick +1
We examine the last-iterate convergence rate of Bregman proximal methods - from mirror descent to mirror-prox and its optimistic variants - as a function of the local geometry indu…