Proves CLT for stochastic gradient non-reversible Langevin Monte Carlo and sufficient condition for variance reduction via anti-symmetric perturbation relative to reversible baseline.
Non-convex stochastic optimization via non-reversible stochastic gradient Langevin dynamics
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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.
A unified large deviations analysis is proposed to study acceleration mechanisms in variants of overdamped Langevin Monte Carlo methods, supported by numerical experiments.
citing papers explorer
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Variance Reduction for Stochastic Gradient Generalized Non-reversible Langevin Monte Carlo Algorithms
Proves CLT for stochastic gradient non-reversible Langevin Monte Carlo and sufficient condition for variance reduction via anti-symmetric perturbation relative to reversible baseline.
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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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Accelerating Langevin Monte Carlo Sampling: A Large Deviations Analysis
A unified large deviations analysis is proposed to study acceleration mechanisms in variants of overdamped Langevin Monte Carlo methods, supported by numerical experiments.