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cs.LG2026
High-Probability PL-SGD with Markovian Noise: Optimal Mixing and Tail Dependence
Dhruv Sarkar, Aprameyo Chakrabartty, Vaneet Aggarwal
We study first-order methods for smooth objectives satisfying the Polyak-Łojasiewicz (PL) condition when gradient samples are generated by an exogenous Markov chain. In the light-t…
cs.LG2025
Projection-free Algorithms for Online Convex Optimization with Adversarial Constraints
Dhruv Sarkar, Aprameyo Chakrabartty, Subhamon Supantha +2
We study a generalization of the Online Convex Optimization (OCO) framework with time-varying adversarial constraints. In this setting, at each round, the learner selects an action…