Kernel Thinning and Stein Thinning
Speaker
Lester Mackey, Stanford University
This talk will introduce two new tools for summarizing a probability distribution more effectively than independent sampling or standard Markov chain Monte Carlo thinning:
- 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.
- 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.
Lester Mackey’s website
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Topic: Yale S&DS Department Seminar
Time: 4:00pm - 5:00pm
Password: 24
Or Telephone：203-432-9666 (2-ZOOM if on-campus) or 646 568 7788
Meeting ID: 991 6970 0816