Safe anytime-valid inference (SAVI) provides measures of statistical evidence and certainty—
e-processes for testing and confidence sequences for estimation—that remain valid at all …

We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical
problem of estimating a bounded mean. Our approach generalizes and improves on the …

We present a unified framework for deriving PAC-Bayesian generalization bounds. Unlike
most previous literature on this topic, our bounds are anytime-valid (ie, time-uniform) …

This paper studies offline policy learning, which aims at utilizing observations collected a
priori (from either fixed or adaptively evolving behavior policies) to learn the optimal …

We provide practical, efficient, and nonparametric methods for auditing the fairness of
deployed classification and regression models. Whereas previous work relies on a fixed …

1 Methods Consider a sequence of random variables (X t) t= 1∞⁠, drawn from a distribution
P∈ P μ⁠, where P μ represents the set of all distributions on [0, 1]∞⁠. We assume EP [X t| X …

Consider two forecasters, each making a single prediction for a sequence of events over
time. We ask a relatively basic question: how might we compare these forecasters, either …

We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical
problem of estimating a bounded mean. Our approach generalizes and improves on the …

We introduce the notion of risk-limiting financial audits (RLFA): procedures that manually
evaluate a subset of $ N $ financial transactions to check the validity of a claimed assertion …

This work derives methods for performing nonparametric, nonasymptotic statistical inference
for population means under the constraint of local differential privacy (LDP). Given bounded …