Parametric learning for directed graphical models

Date: February 24, 2022
Time: 4:00 pm
Room: DBH 4011
Speaker: Leonard J. Schulman 

The following problem has long been studied in the TCS literature: learn
a probability distribution on n bits which is known to be a mixture of k
distributions, in each of which the bits are independent. This (as a
parameter-identification problem) is intermediate in expressiveness
between the much simpler case of iid bits, and the much more ambitious
case in which the bits are not independent at all, but are related
through a Causal Bayesian Network. I’ll give an overview of a series of
works showing how to
(a) Reduce the network case to the independent case (by a conditioning
(b) Reduce the independent case to the iid case (by an algorithm which
creates “synthetic bits”), and finally,
(c) Give upper and lower bounds for the iid case (the algorithm here is
the two-century-old method of Prony).

Based on joint works with Chaitanya Swamy (Waterloo), Yuval Rabani
(Hebrew U), Jian Li (Tsinghua), Spencer Gordon (Caltech) and Bijan
Mazaheri (Caltech)


Bio: Leonard J. Schulman received the B.Sc. in Mathematics in 1988 and
the Ph.D. in Applied Mathematics in 1992, both from the Massachusetts
Institute of Technology. Since 2000 he has been on the faculty of the
California Institute of Technology. He has also held appointments at UC
Berkeley, the Weizmann Institute of Science, the Georgia Institute of
Technology, and the Israel Institute for Advanced Studies.

Close Menu
Skip to content