37 citations · 57 across the 6 of their papers we have counts for
7 papers
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization
Ali Kavis, Stratis Skoulakis, Kimon Antonakopoulos +2
We propose an adaptive variance-reduction method, called AdaSpider, for minimization of -smooth, non-convex functions with a finite-sum structure. In essence, AdaSpider combines…
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize
Ali Kavis, Kfir Yehuda Levy, Volkan Cevher
In this paper, we propose a new, simplified high probability analysis of AdaGrad for smooth, non-convex problems. More specifically, we focus on a particular accelerated gradient (…
STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization
Kfir Y. Levy, Ali Kavis, Volkan Cevher
In this work we investigate stochastic non-convex optimization problems where the objective is an expectation over smooth loss functions, and the goal is to find an approximate sta…
Double-Loop Unadjusted Langevin Algorithm
Paul Rolland, Armin Eftekhari, Ali Kavis +1
A well-known first-order method for sampling from log-concave probability distributions is the Unadjusted Langevin Algorithm (ULA). This work proposes a new annealing step-size sch…
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis +1
This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm's convergence properties in non-convex problems. We first show that the s…
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…