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New method improves gradient estimation for Markov chains

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel method for unbiasedly estimating gradients of stationary means in parameterized Markov chains. This new approach is particularly effective for chains that mix slowly and can be applied to parametrizations involving neural networks. The method requires an oracle to evaluate the transition density and its gradient, potentially leading to significant efficiency gains, as supported by theoretical predictions and numerical experiments. AI

IMPACT This research could enhance the efficiency of training complex machine learning models that utilize Markov chain properties.

RANK_REASON The cluster contains an academic paper detailing a new research methodology.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Jeffrey Wang, Chang-han Rhee ·

    Unbiased Derivative Estimation for Stationary Mean of Parameterized Markov chains

    arXiv:2606.11487v1 Announce Type: cross Abstract: We propose a new approach to unbiased estimation of the gradients of the stationary means associated with parametrized families of Markov chains. Our estimators are particularly efficient when the Markov chains have slow mixing ra…

  2. arXiv stat.ML TIER_1 English(EN) · Chang-han Rhee ·

    Unbiased Derivative Estimation for Stationary Mean of Parameterized Markov chains

    We propose a new approach to unbiased estimation of the gradients of the stationary means associated with parametrized families of Markov chains. Our estimators are particularly efficient when the Markov chains have slow mixing rate. Our approach does not require a specific param…

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