Decentralized SGD and SGDA under Markovian sampling admit non-asymptotic generalization bounds that incorporate network topology, Markov mixing rates, and primal-dual dynamics.
IEEE Transactions on Automatic Control , volume=
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A novel bias-reduced online covariance estimator for SGD achieves convergence rate n to the power (α-1)/2 times square root of log n without second-order derivatives.
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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.
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Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction
A novel bias-reduced online covariance estimator for SGD achieves convergence rate n to the power (α-1)/2 times square root of log n without second-order derivatives.