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cs.LG2024
Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD
Jie Hu, Yi-Ting Ma, Do Young Eun
Distributed learning is essential to train machine learning algorithms across heterogeneous agents while maintaining data privacy. We conduct an asymptotic analysis of Unified Dist…
stat.ML2024
Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
Jie Hu, Vishwaraj Doshi, Do Young Eun
Two-timescale stochastic approximation (TTSA) is among the most general frameworks for iterative stochastic algorithms. This includes well-known stochastic optimization methods suc…
math.PR2024★ 1 cited
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
Jie Hu, Vishwaraj Doshi, Do Young Eun
We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion. Typically, these r…