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Central limit theorem for two-timescale stochastic algorithms

Paper Authors:

Jie Hu,

Vishwaraj Doshi,

Do Young Eun

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Key Details

Establishes central limit theorem for nonlinear two-timescale stochastic algorithms

Uncovers effect of underlying Markov chain on asymptotic behavior

Expands sampling efficiency strategies from SGD to bilevel, minimax optimization

Shows nonlinear GTD algorithms have identical asymptotic covariance

AI generated summary

Central limit theorem for two-timescale stochastic algorithms

This paper establishes a central limit theorem for two-timescale stochastic algorithms under Markovian noise. Key contributions include uncovering the coupled dynamics influenced by the Markov chain, expanding efficient sampling strategies from SGD to broader applications, and deducing identical asymptotic performance of nonlinear GTD algorithms.

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