Timezone: »

Sequential Kernelized Independence Testing
Aleksandr Podkopaev · Patrick Bloebaum · Shiva Kasiviswanathan · Aaditya Ramdas

Tue Jul 25 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #619

Independence testing is a classical statistical problem that has been extensively studied in the batch setting when one fixes the sample size before collecting data. However, practitioners often prefer procedures that adapt to the complexity of a problem at hand instead of setting sample size in advance. Ideally, such procedures should (a) stop 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. Classical batch tests are not tailored for streaming data: valid inference after data peeking requires correcting for multiple testing which results in low power. Following the principle of testing by betting, we design sequential kernelized independence tests that overcome such shortcomings. We exemplify our broad framework using bets inspired by kernelized dependence measures, e.g., the Hilbert-Schmidt independence criterion. Our test is also valid under non-i.i.d. time-varying settings. We demonstrate the power of our approaches on both simulated and real data.

Author Information

Aleksandr Podkopaev (Carnegie Mellon University)
Patrick Bloebaum (Amazon Web Services)
Shiva Kasiviswanathan (Amazon)
Aaditya Ramdas (Carnegie Mellon University)

More from the Same Authors