Search Results for author:
Found 5 papers, 4 papers with code
Anytime-valid off-policy inference for contextual bandits
Importantly, our methods can be employed while the original experiment is still running (that is, not necessarily post-hoc), when the logging policy may be itself changing (due to learning), and even if the context distributions are a highly dependent time-series (such as if they are drifting over time).
A nonparametric extension of randomized response for private confidence sets
This work derives methods for performing nonparametric, nonasymptotic statistical inference for population means under the constraint of local differential privacy (LDP).
Time-uniform central limit theory, asymptotic confidence sequences, and anytime-valid causal inference
While the CLT approximates the distribution of a sample average by that of a Gaussian at a fixed sample size, we use strong invariance principles (stemming from the seminal 1970s work of Komlos, Major, and Tusnady) to uniformly approximate the entire sample average process by an implicit Gaussian process.
Estimating means of bounded random variables by betting
This paper derives confidence intervals (CI) and time-uniform confidence sequences (CS) for the classical problem of estimating an unknown mean from bounded observations.
Confidence sequences for sampling without replacement
We then present Hoeffding- and empirical-Bernstein-type time-uniform CSs and fixed-time confidence intervals for sampling WoR, which improve on previous bounds in the literature and explicitly quantify the benefit of WoR sampling.
