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A general recipe for data-adaptive anytime-valid envelopes for iterative algorithms

Bonnerjee, Soham et al.
arXiv (All) · Papers
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machine-learningmethodologyoptimization-and-control
Most convergence guarantees for iterative stochastic algorithms are stated at a fixed iteration count and do not justify stopping at a data-dependent time. Recent work has constructed anytime-valid envelopes that do, but deterministic envelopes are typically too conservative for practical stopping,
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