Seminars
View all Seminars | Download ICal for this eventEfficient Distance Approximation for Structured High-Dimensional Distributions via Learning
Series: Department Seminar
Speaker: Dr. Arnab Bhattacharyya (NUS Singapore, School of Computing)
Date/Time: Feb 18 11:15:00
Location: CSA Seminar Hall (Room No. 254, First Floor)
Faculty Advisor:
Abstract:
We design efficient distance approximation algorithms for several classes of structured
high-dimensional distributions. Specifically, we show algorithms for the following problems:
– Given sample access to two Bayes networks P1 and P2 over known directed acyclic graphs
G1 and G2 having n nodes and bounded in-degree, approximate dTV (P1, P2) to within additive
error ε using poly(n, ε) samples and time
– Given sample access to two ferromagnetic Ising models P1 and P2 on n variables with bounded
width, approximate dTV (P1, P2) to within additive error ε using poly(n, ε) samples and time
– Given sample access to two n-dimensional gaussians P1 and P2, approximate dTV (P1, P2)
to within additive error ε using poly(n, ε) samples and time
– Given access to observations from two causal models P and Q on n variables that are
defined over known causal graphs, approximate dTV (Pa, Qa) to within additive error ε
using poly(n, ε) samples, where Pa and Qa are the interventional distributions obtained
by the intervention do(A = a) on P and Q respectively for a particular variable A.
Our results are the first efficient distance approximation algorithms for these well-studied
problems. They are derived using a simple and general connection to distribution learning
algorithms. The distance approximation algorithms imply new efficient algorithms for tolerant
testing of closeness of the above-mentioned structured high-dimensional distributions.
(based on a joint work with Sutanu Gayen, Kuldeep S. Meel and N. V. Vinodchandran)
Speaker Bio:
Host Faculty: Sunil Chandran L