High-probability generalization bounds for D-SGD are derived at the optimal rate O(1/sqrt(mn) log(1/δ)) via pointwise uniform stability across convex and non-convex settings.
SIAM Journal on Optimization , volume=
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DE-PSGLD is the first decentralized MCMC sampler for constrained convex domains that converges to a regularized Gibbs distribution with explicit 2-Wasserstein bounds for agents and network averages.
Decentralized SGD and SGDA under Markovian sampling admit non-asymptotic generalization bounds that incorporate network topology, Markov mixing rates, and primal-dual dynamics.
citing papers explorer
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Unveiling High-Probability Generalization in Decentralized SGD
High-probability generalization bounds for D-SGD are derived at the optimal rate O(1/sqrt(mn) log(1/δ)) via pointwise uniform stability across convex and non-convex settings.
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Decentralized Proximal Stochastic Gradient Langevin Dynamics
DE-PSGLD is the first decentralized MCMC sampler for constrained convex domains that converges to a regularized Gibbs distribution with explicit 2-Wasserstein bounds for agents and network averages.
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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.