Join ECE for this year’s Ian F. Blake Lectureship speaker, Professor Vahid Tarokh from Duke University, on Wednesday, May 10th at 11am PDT, online.
Tarokh’s lecture “Hypothesis Testing and Quickest Change Detection for Unnormalized Models” presents a fresh take on likelihood-based hypothesis testing, reflecting on first experiences with this topic from his graduate Digital Communications course taught by Ian Blake.
The celebrated Neyman-Pearson Lemma proves that this test is Universally Most Powerful for testing a null hypothesis versus an alternative one. Similarly, the CUSUM change detection is based on log-likelihood ratio and its optimality has been proved by Moustakides. In order to apply these optimal tests, we need to know the exact pdfs for both null and alternative (respectively pre and post change) distributions. These pdfs are not unfortunately easy to obtain in high dimensional data-driven scenarios. Recent research has demonstrated that energy, score-based and diffusion methods produce state of the art models in high-dimensional setting. Additionally, these models are extremely robust to potential noises in the collected data. Unfortunately, these models are unnormalized. Calculating the partition functions required for their normalization is a notoriously difficult problem. This limits their applicability to log-likelihood based tests.
This motivates our work where we have developed Fisher-inspired methods for hypothesis testing, quickest change detection, and their robust versions (when the hypotheses or pre- and post-change distributions may not be exactly known) for unnormalized models. We will discuss our results and demonstrate their applications to various scenarios including out of distribution detection.
About the speaker:
Vahid Tarokh received the PhD degree from the University of Waterloo in 1995 under the supervision of Ian F. Blake. He worked at AT&T Labs-Research until 2000. From 2000-2002, he was an Associate Professor at Massachusetts Institute of Technology (MIT). In 2002, he joined Harvard University as a Hammond Vinton Hayes Senior Fellow of Electrical Engineering and Perkins Professor of Applied Mathematics. He joined Duke University in Jan 2018, as the Rhodes Family Professor of Electrical and Computer Engineering. He was also a Gordon Moore Distinguished Research Fellow at CALTECH in 2018. Between Jan 2019-Dec 2021, he was also a Microsoft Data Science Investigator at Duke University.
Please RSVP to attend and receive the Zoom link: https://ubc.ca1.qualtrics.com/jfe/form/SV_b3m9cLPhD7Pdbgi