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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.LG2026
Bringing Order to Asynchronous SGD: Towards Optimality under Data-Dependent Delays with Momentum
Tehila Dahan, Roie Reshef, Sharon Goldstein +1
Asynchronous SGD enables scalable distributed training but suffers from stale gradients. When delays depend on the data, slower samples may be underrepresented, biasing training to…
cs.LG2026
Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum
Navdeep Kumar, Tehila Dahan, Lior Cohen +4
We establish an optimal sample complexity of for obtaining an -optimal global policy using a single-timescale actor-critic (AC) algorithm in infinite-horizon discoun…