Sequential Kernelized Independence Testing
Speaker: Sasha Podkopaev
Abstract: Independence testing is a fundamental and classical statistical problem that has been extensively studied in the batch setting when one fixes the sample size before collecting data. Instead of sticking to a prespecified sample size, well-designed sequential tests (a) allow stopping earlier on easy tasks (and later on harder tasks), hence making better use of available resources, and (b) continuously monitor the data and efficiently incorporate statistical evidence after collecting new data, while controlling the false alarm rate. In this talk, I will talk about our recent work on using the principle of testing by betting and kernel methods for consistent sequential nonparametric independence testing. After discussing the necessary background on batch independence testing and highlighting the respective limitations, I will present our new test and interpret the results. Lastly, I will briefly discuss extensions to those settings, where batch independence tests fail, such as testing under non-i.i.d. time-varying settings. Link: https://arxiv.org/abs/2212.07383