ICML 2026 Hypothesis Testing Workshop

Hypothesis testing, while much-maligned, remains a key component of scientific practice. Machine learning contributes to helping develop testing methodology, with many key advances in testing coming from the ML community, from widely used nonparametric tests to recent work on e-values. Machine learning practice can also benefit from the usage of hypothesis testing techniques, whether for checking or ensuring model reliability and robustness, or practical methods for helping detect subgroup shifts in medical applications. This workshop will explore advances both in testing methodology and in its impacts across ML.