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math.OC2024
Open Problem: Anytime Convergence Rate of Gradient Descent
Guy Kornowski, Ohad Shamir
Recent results show that vanilla gradient descent can be accelerated for smooth convex objectives, merely by changing the stepsize sequence. We show that this can lead to surprisin…
math.OC2023★ 3 cited
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
Nikita Kornilov, Ohad Shamir, Aleksandr Lobanov +5
In this paper, we consider non-smooth stochastic convex optimization with two function evaluations per round under infinite noise variance. In the classical setting when noise has…
math.OC2016★ 4 cited
Oracle Complexity of Second-Order Methods for Finite-Sum Problems
Yossi Arjevani, Ohad Shamir
Finite-sum optimization problems are ubiquitous in machine learning, and are commonly solved using first-order methods which rely on gradient computations. Recently, there has been…