66 citations · 228 across the 17 of their papers we have counts for
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math.OC2024★ 1 cited
Tuning-Free Stochastic Optimization
Ahmed Khaled, Chi Jin
Large-scale machine learning problems make the cost of hyperparameter tuning ever more prohibitive. This creates a need for algorithms that can tune themselves on-the-fly. We forma…
math.OC2020★ 31 cited
Random Reshuffling: Simple Analysis with Vast Improvements
Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
Random Reshuffling (RR) is an algorithm for minimizing finite-sum functions that utilizes iterative gradient descent steps in conjunction with data reshuffling. Often contrasted wi…
math.OC2020★ 59 cited
Better Theory for SGD in the Nonconvex World
Ahmed Khaled, Peter Richtárik
Large-scale nonconvex optimization problems are ubiquitous in modern machine learning, and among practitioners interested in solving them, Stochastic Gradient Descent (SGD) reigns…