Establishes non-asymptotic Gaussian approximation bounds for federated LSA with explicit communication-heterogeneity trade-offs and introduces an online multiplier bootstrap for last-iterate inference with validity guarantees.
Journal of the Operational Research Society , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Policy iteration for discounted robust MDPs is strongly polynomial for L1 and L∞ uncertainty sets but hard for other Lp sets.
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Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
Establishes non-asymptotic Gaussian approximation bounds for federated LSA with explicit communication-heterogeneity trade-offs and introduces an online multiplier bootstrap for last-iterate inference with validity guarantees.
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On the Complexity of Discounted Robust MDPs with $L_p$ Uncertainty Sets
Policy iteration for discounted robust MDPs is strongly polynomial for L1 and L∞ uncertainty sets but hard for other Lp sets.