62 citations · 424 across the 139 of their papers we have counts for
15 papers · 1 filter
Scalable Semidefinite Programming
Alp Yurtsever, Joel A. Tropp, Olivier Fercoq +2
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct ra…
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
Donghwan Lee, Niao He, Parameswaran Kamalaruban +1
This article reviews recent advances in multi-agent reinforcement learning algorithms for large-scale control systems and communication networks, which learn to communicate and coo…
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
Ali Kavis, Kfir Y. Levy, Francis Bach +1
We propose a novel adaptive, accelerated algorithm for the stochastic constrained convex optimization setting. Our method, which is inspired by the Mirror-Prox method, \emph{simult…
Nearly Minimal Over-Parametrization of Shallow Neural Networks
Armin Eftekhari, ChaeHwan Song, Volkan Cevher
A recent line of work has shown that an overparametrized neural network can perfectly fit the training data, an otherwise often intractable nonconvex optimization problem. For (ful…
A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators
Volkan Cevher, Bang Cong Vu
The proximal extrapolated gradient method \cite{Malitsky18a} is an extension of the projected reflected gradient method \cite{Malitsky15}. Both methods were proposed for solving th…
Fast and Provable ADMM for Learning with Generative Priors
Fabian Latorre Gómez, Armin Eftekhari, Volkan Cevher
In this work, we propose a (linearized) Alternating Direction Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject to a nonconvex constraint. We focus on…