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This paper provides worst-case convergence analyses for Schedule-Free gradient descent and stochastic gradient descent in nonconvex optimization, establishing optimal rates and strict-saddle avoidance, thus theoretically justifying their empirical success.
This paper studies the sample complexity of learning in average-reward weakly-coupled MDPs and restless bandits, establishing finite-sample PAC guarantees with polynomial complexity using a novel Lyapunov-based analysis framework.