9 citations · 29 across the 18 of their papers we have counts for
7 papers · 1 filter
Confidence Horizons
Chase Mathis, Ian Waudby-Smith
Anytime-valid inference enables analysts to continuously monitor their data and stop experiments early. However, the majority of these methods incur a certain conservativeness by r…
Multi-Armed Sequential Hypothesis Testing by Betting
Ricardo J. Sandoval, Ian Waudby-Smith, Michael I. Jordan
We consider a variant of sequential testing by betting where, at each time step, the statistician is presented with multiple data sources (arms) and obtains data by choosing one of…
Combining e-values using demi-supermartingales
Jiahao Ming, Aaditya Ramdas, Yi Shen +2
We present a new method for combining e-variables through demi-supermartingales, which settles an old conjecture in the literature on nonparametric mean testing. It also provides a…
On Nonasymptotic Confidence Intervals for Treatment Effects in Randomized Experiments
Ricardo J. Sandoval, Sivaraman Balakrishnan, Avi Feller +2
We study nonasymptotic (finite-sample) confidence intervals for treatment effects in randomized experiments. In the existing literature, the effective sample sizes of nonasymptotic…
Anytime-valid off-policy inference for contextual bandits
Ian Waudby-Smith, Lili Wu, Aaditya Ramdas +2
Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in healthcare and the tech industry. They involve online learning algorithms that adaptively…
Nonparametric extensions of randomized response for private confidence sets
Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas
This work derives methods for performing nonparametric, nonasymptotic statistical inference for population means under the constraint of local differential privacy (LDP). Given bou…
Confidence sequences for sampling without replacement
Ian Waudby-Smith, Aaditya Ramdas
Many practical tasks involve sampling sequentially without replacement (WoR) from a finite population of size , in an attempt to estimate some parameter . Accurately qu…