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.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=
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A proximal gradient sampler for composite log-concave distributions achieves near-optimal iteration complexity of order kappa sqrt(d) log^4(1/epsilon) in total variation distance under strong convexity and smoothness.
Geometric tempering yields exponential convergence bounds for both Wasserstein and Fisher-Rao flows but produces no speedup in the Fisher-Rao metric, with new adaptive schedules derived from the tempered dynamics.
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A proximal gradient algorithm for composite log-concave sampling
A proximal gradient sampler for composite log-concave distributions achieves near-optimal iteration complexity of order kappa sqrt(d) log^4(1/epsilon) in total variation distance under strong convexity and smoothness.