New Method for Unbiased Gradient Estimation in Parameterized Markov Chains
Researchers have proposed a new approach to unbiased estimation of gradients of stationary means for parameterized families of Markov chains. The method is designed to perform well even when Markov chains mix slowly, and requires only an oracle to evaluate the transition density and its gradient at a given point. This is significant because it makes the estimator compatible with neural network parameterizations without requiring full knowledge of the density function.
A preliminary preprint posted to arXiv introduces a novel estimator for computing unbiased derivatives of stationary means in parameterized Markov chains. The approach is specifically engineered to be efficient in the challenging regime where Markov chains exhibit slow mixing rates, a common bottleneck in many statistical and machine learning applications. Unlike prior methods, it imposes minimal structural requirements on the parameterization: it needs only an oracle capable of evaluating the transition density and its gradient at a given data point. This design choice makes the estimator broadly applicable, including to models parameterized by neural networks. The authors report that numerical experiments support the theoretical efficiency gains predicted by their analysis. The paper is described as a preliminary draft, with a full version in preparation.
What's missing
As a preliminary draft, the paper has not yet undergone peer review. The full version is still in preparation, meaning theoretical results, proofs, and experimental comparisons may be incomplete or subject to revision. The abstract does not specify the classes of Markov chains or mixing rate regimes for which the efficiency improvements are quantified, nor does it compare directly against existing state-of-the-art gradient estimators in concrete benchmarks.
What different sources said
- arXiv stat.MLCenter
Unbiased Derivative Estimation for Stationary Mean of Parameterized Markov chains
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