Lester Mackey: Kernel Thinning and Stein Thinning

Machine Learning Seminar presentation

Topic: Kernel Thinning and Stein Thinning

Speaker: Lester Mackey, a researcher at Microsoft Research New England, and an adjunct professor at Stanford University.

Time: Wednesday, 2022.02.23, 15:00 CET

How to join: Please contact Jakub Lengiewicz

Abstract:

This talk will introduce two new tools for summarizing a probability distribution more effectively than independent sampling or standard Markov chain Monte Carlo thinning:

1. Given an initial n point summary (for example, from independent sampling or a Markov chain), kernel thinning finds a subset of only square-root n points with comparable worst-case integration error across a reproducing kernel Hilbert space.

2. If the initial summary suffers from biases due to off-target sampling, tempering, or burn-in, Stein thinning simultaneously compresses the summary and improves the accuracy by correcting for these biases.

These tools are especially well-suited for tasks that incur substantial downstream computation costs per summary point like organ and tissue modeling in which each simulation consumes 1000s of CPU hours.

Additional material:

Video recording: https://youtu.be/91p4Octzc6E