We study normalized error feedback algorithms with momentum and parameter-agnostic stepsizes, eliminating the need for problem-dependent tuning while achieving competitive convergence rates.
Nov 1, 2025
We introduce Bernoulli-LoRA, a theoretical framework for randomized Low-Rank Adaptation that unifies existing approaches and provides convergence guarantees for various optimization methods.
Aug 1, 2025
We extend MARINA-P algorithm to non-smooth federated optimization, providing the first theoretical analysis with server-to-worker compression and adaptive stepsizes while achieving optimal convergence rates.
Dec 22, 2024