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arXiv paper analyzes stationary bias in nonlinear two-timescale stochastic approximation

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This paper delves into the stationary bias present in nonlinear two-timescale stochastic approximation methods. The research focuses on recursions driven by finite-state Markov chains, deriving a first-order bias expansion that remains uniform even when the slow step size is significantly smaller than the fast step size. The findings indicate that the bias reduction strategy requires careful matching of Richardson-Romberg extrapolation weights to the specific step-size path, as bias exponents may not always be integers. The study includes an exactly solvable nonlinear Markov example for verification and examines temporal-difference learning and finite-run extrapolation. AI

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv paper analyzes stationary bias in nonlinear two-timescale stochastic approximation

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · A. Ch. Madhusudanarao, Rahul Singh ·

    Stationary Bias and Extrapolation in Nonlinear Two-Timescale Stochastic Approximation

    arXiv:2610.10246v1 Announce Type: new Abstract: Constant-step stochastic approximation generally has a nonzero stationary mean error that persists under time averaging. This paper studies that error for nonlinear two-timescale recursions driven by an exogenous finite-state Markov…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Stationary Bias and Extrapolation in Nonlinear Two-Timescale Stochastic Approximation

    Constant-step stochastic approximation generally has a nonzero stationary mean error that persists under time averaging. This paper studies that error for nonlinear two-timescale recursions driven by an exogenous finite-state Markov chain. Under stated smoothness assumptions and …

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