11h ago
Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
★★★★★
significance 2/5
The paper introduces Score-Repellent Monte Carlo (SRMC), a new framework for efficient non-Markovian sampling in general state spaces. It uses a running average of score evaluations to reduce variance and improve sampling efficiency without the high memory costs of traditional history-dependent methods.
Why it matters
Optimizing non-Markovian sampling efficiency offers a pathway to more stable and memory-efficient generative modeling in complex state spaces.
Tags
#monte carlo #sampling #mcmc #stochastic approximation #variance reductionRelated coverage
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