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.
International Conference on Artificial Intelligence and Statistics , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.
citing papers explorer
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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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Robust Simulation Based Inference Through Robust Optimal Transport
Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.