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Achieve oracle-level performance in multi-armed sequential hypothesis testing by betting, even without knowing which arm provides the most evidence.
Achieve 13-15% more efficient LLM watermark detection by using e-values for anytime-valid inference, enabling early stopping without sacrificing statistical guarantees.
Forget global worst-case guarantees – this new multi-objective learning method actually adapts to distribution shifts and delivers unbiased predictions in dynamic environments.
