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
We introduce three point compressors (3PC), a novel framework unifying and improving communication-efficient distributed optimization methods. Presented as a Poster at ICML 2022.
Feb 2, 2022
Six practical algorithmic extensions of the EF21 error feedback method for communication-efficient distributed learning, with strong convergence guarantees.
Oct 7, 2021
We propose EF21, a novel approach to error feedback offering a better theoretical rate and strong empirical results. Presented as an Oral + Poster at NeurIPS 2021.
Jun 9, 2021