Researchers have demonstrated a method to aggregate asymptotically optimal sequential tests into log-optimal e-processes. This work proves the converse of a previous finding, establishing that optimal sequential tests can indeed be constructed from these e-processes. The new approach utilizes a novel class of WAIT e-processes, which are weighted aggregates of indicators of stopping times. AI
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IMPACT This research advances theoretical understanding in sequential testing, which could have downstream implications for AI systems that require efficient decision-making under uncertainty.
RANK_REASON The cluster contains an academic paper published on arXiv detailing a new theoretical result in statistics.