E-Values: From Statistics to ML

E-values provide a type of uncertainty quantification that is far more robust and flexible than classical measures (e.g., p-values): it enables anytime-valid statistical inference that have Type-I error guarantees under continuous monitoring. Unlike classical methods, e-values come with meaningful risk guarantees at adaptively chosen significance levels, allow for principled use of prior information without sacrificing validity, and play a foundational role in multiple testing. Building upon these recent breakthroughs on e-values---which have largely been concentrated in theoretical statistics---this workshop aims to bring these advances to the machine learning world and build a robust research community at the intersection of e-values and ML. We identify several key topics of interest, including the connections to conformal prediction; Bayesian and pseudo-Bayesian methods; bandit and adversarial learning; multiple testing; and modern ML applications such as auditing of LLMs and AI systems. The will be the first workshop dedicated to e-values at a top ML conference, featuring speakers with diverse interests ranging from multiple testing and conformal prediction to bandit applications and economics.