Testing properties of distributions
Speaker: CassandraTitle: Testing properties of distributions
Date: 03 Nov 2022 17:30-19:00 EST
Location: SEC Level 3 NW Terrace
Food: Tacos
Distribution testing is an area of research at the intersection of statistics and theoretical computer science that is concerned with testing properties of distributions using a small number of samples. Many applications today rely on a huge amount of data that is supported over a large domain. This motivates the design of sample-efficient algorithms to test specific properties of distributions, since standard algorithms from learning theory or statistics may be too costly in this setting. An example of a specific property someone may want to test is if their data is uniformly distributed.
In this talk, I will first introduce distribution testing, including the model, its relationship to learning, and how it compares to the general property testing framework. After this, I will introduce an algorithm for testing equality to the uniform distribution. I will present a proof that the uniform distribution is complete with respect to testing equality to a fixed distribution, which gives sample complexity bounds for testing equality to any fixed distribution.
This talk includes content from Chapter 11 of “Introduction to Property Testing” by Goldreich.