Proves almost sure convergence and finite-time sample complexity bounds for stochastic mirror descent under iterate-dependent Markov noise for both convex and non-convex objectives.
2002.02873 , archivePrefix=
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Decentralized SGD and SGDA under Markovian sampling admit non-asymptotic generalization bounds that incorporate network topology, Markov mixing rates, and primal-dual dynamics.
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Stochastic Mirror Descent under Iterate-Dependent Markov Noise: Analysis in the Asymptotic and Finite Time Regimes
Proves almost sure convergence and finite-time sample complexity bounds for stochastic mirror descent under iterate-dependent Markov noise for both convex and non-convex objectives.
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Stability and Generalization for Decentralized Markov SGD
Decentralized SGD and SGDA under Markovian sampling admit non-asymptotic generalization bounds that incorporate network topology, Markov mixing rates, and primal-dual dynamics.