Stanford MLSys Seminar Episode 40: Dennis Shasha & Mustafa Anil Kocak [Th, 1.35-2.30pm PT]

[Karan Goel]

Hi everyone,

We're back with the fortieth episode of the MLSys Seminar on Thursday from 1.35-2.30pm PT. 

We'll be joined by Dennis Shasha and Mustafa Anil Kocak, who will talk about building meta-algorithms for AI safety. The format is a 30 minute talk followed by a 30 minute podcast-style discussion, where the live audience can ask questions.

Livestream Link: https://www.youtube.com/watch?v=W2q-8MiW8no

Guests: Dennis Shasha and Mustafa Anil Kocak

Title: SafePredict and Friends

Abstract: SafePredict is a meta-algorithm for machine learning applications that strategically refuses to accept the predictions of an underlying machine learning algorithm or algorithms. The goal is to achieve a user-specified correctness rate on the non-refused predictions without refusing too much. We show applications to an on-line learning setting in which the data-to-class mapping is not independent and identically distributed (not iid). In related work, we look at classification problems where we are willing to guess, on average, k classes in the hopes that one is correct. We compare such an approach in which we always choose the top k most likely classes. Finally, we consider the problem of selective sampling in settings where evaluating each sample is expensive. We build on and improve the Horvitz-Thompson and Augmented Inverse Probability Weighted sampling methods.

Bios: 

Dennis Shasha is a Julius Silver Professor of computer science at the Courant Institute of New York University and an Associate Director of NYU Wireless. He works on meta-algorithms for machine learning to achieve guaranteed correctness rates, with biologists on pattern discovery for network inference; on automated verification for concurrent algorithms; on a tool for policy planners facing epidemics; on tree and graph matching; on algorithms for time series for finance and migratory patterns; on database tuning; and on computational reproducibility. Because he likes to type, he has written six books of puzzles about a mathematical detective named Dr. Ecco, a biography about great computer scientists, and a book about the future of computing. He has also written eight technical books about database tuning, biological pattern recognition, time series, DNA computing, resampling statistics, causal inference in molecular networks, and the automated verification of concurrent search structures. He has co-authored more than 85 journal papers, 80 conference papers, and 25 patents. Because he loves puzzles, he has written the puzzle column for various publications including Scientific American, Dr. Dobb's Journal, and currently the Communications of the ACM. He is a fellow of the ACM and an INRIA International Chair.

Mustafa A. Kocak received the BSc degree in electrical engineering from Bilkent University, Ankara, Turkey, and the PhD degree from the NYU School of Engineering. He is a computational biologist in Broad Institute of MIT and Harvard. His research interests include machine learning applications, information theory, and biostatistics. His current efforts are focused on biomarker analysis and target deconvolution for pre-clinical drug development for cancer, and data processing methods for high-throughput experimental data.

See you all there!

Best,

Karan

[Karan Goel]

Reminder: we're starting in 10 minutes!