15 citations · 54 across the 38 of their papers we have counts for
5 papers · 1 filter
Clipped or Unclipped? Finite-Sample Trade-offs for Averaged SGD under Heavy-Tailed Noise
Alexandra Suvorikova, Egor Gladin, Darina Dvinskikh +5
Gradient clipping is widely used to stabilize training, but it need not improve the statistical accuracy of averaged SGD, even under heavy-tailed noise. We derive a finite-sample c…
Iterative Methods for Computing the Moore--Penrose Inverse of Split-Quaternion Matrices with Applications
Salman Ahmadi-Asl, Valentin Leplat, Mohammad S. Alkousa
We study iterative methods for computing the Moore--Penrose inverse of split-quaternion matrices. We first establish a consistent framework based on a \(2\times2\) real representat…
Mirror Descent Methods for Quasar Convex Optimization Problems With Non-Smooth Inequality Constraints
Mohammad Alkousa
In this paper, we consider constraint optimization problems subject to non-smooth convex functional (inequality-type) constraints, wherein the objective function is non-smooth and…
Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints
Mohammad S. Alkousa, Fedor S. Stonyakin, Belal A. Alashqar +1
Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial training, and generative models.…
Lipschitz-Free Mirror Descent Methods for Relatively Strongly Convex Functions with/without Absolute and Relative Inexactness
Mohammad S. Alkousa, Fedor S. Stonyakin
In this paper, we analyze the mirror descent algorithm for non-smooth optimization problems in which the objective function is relatively strongly convex, without relying on the st…