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Adaptive experiments use bandit algorithms to favor interventions with higher expected rewards. However, decision-makers also require reliable inference on current treatment effects: differences between intervention and reference-arm mean rewards at the present time, rather than historical averages. These objectives co...
Borders reveal a noise--drift tradeoff: in noise-dominated regimes, first-moment averaging and adaptive preconditioning can yield favorable upper guarantees, whereas in drift-dominated regimes, stale first-moment information and preconditioning can enlarge Adam's tracking guarantee, potentially allowing vanilla SGD to...
HOPPER is introduced, an end-to-end learnable extension of LGSM that learns how hop sequences should be extracted before processing by a modern state-space model, and demonstrates that learnable sequence extraction is a flexible and effective framework for long-range graph representation learning across synthetic, phys...
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