The next TCS+ talk will take place this coming Wednesday, June 9th at 1:00 PM Eastern Time (10:00 AM Pacific Time, 19:00 Central European Time, 17:00 UTC). Pravesh Kothari and Ankur Moitra from CMU and MIT will (jointly) speak about “Robustly Learning Mixtures of Gaussians” (abstract below).
Note that the seminar will be a bit longer than the usual: it’s a double feature!
You can reserve a spot as an individual or a group to join us live by signing up on the online form. Due to security concerns, registration is required to attend the interactive talk. (The recorded talk will also be posted on our website afterwards, so people who did not sign up will still be able to watch the talk) As usual, for more information about the TCS+ online seminar series and the upcoming talks, or to suggest a possible topic or speaker, please see the website.
Abstract: For a while now the problem of robustly learning a high-dimensional mixture of Gaussians has had a target on its back. The first works in algorithmic robust statistics gave provably robust algorithms for learning a single Gaussian. Since then there has been steady progress, including algorithms for robustly learning mixtures of spherical Gaussians, mixtures of Gaussians under separation conditions, and arbitrary mixtures of two Gaussians. In this talk we will discuss two recent works that essentially resolve the general problem. There are important differences in their techniques, setup, and overall quantitative guarantees, which we will discuss.
The talk will cover the following independent works:
- Liu, Moitra, “Settling the Robust Learnability of Mixtures of Gaussians”
- Bakshi, Diakonikolas, Jia, Kane, Kothari, Vempala, “Robustly Learning Mixtures of Arbitrary Gaussians”