3 papers
cs.LG2026
Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
Tehila Dahan, Bassel Hamoud, Roie Reshef +2
Communication overhead is a crucial bottleneck in scalable distributed learning. While existing methods aim to efficiently utilize data points, such as Local SGD, Minibatch SGD, an…
cs.LG2025
Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning
Ze'ev Zukerman, Bassel Hamoud, Kfir Y. Levy
Distributed learning methods have gained substantial momentum in recent years, with communication overhead often emerging as a critical bottleneck. Gradient compression techniques…
cs.LG2025
Safety in the Face of Adversity: Achieving Zero Constraint Violation in Online Learning with Slowly Changing Constraints
Bassel Hamoud, Ilnura Usmanova, Kfir Y. Levy
We present the first theoretical guarantees for zero constraint violation in Online Convex Optimization (OCO) across all rounds, addressing dynamic constraint changes. Unlike exist…