Past talk · AI/ML Seminar Series
Predictive Querying for Autoregressive Neural Sequence Models
PhD Student, Department of Statistics, University of California, Irvine
- Date & time
- Monday, October 31, 2022 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
In reasoning about sequential events it is natural to pose probabilistic queries such as when will event A occur next or what is the probability of A occurring before B. However, with machine learning shifting towards neural autoregressive models such as RNNs and transformers, probabilistic querying has been largely restricted to simple cases such as next-event prediction, in part because future querying involves marginalization over large path spaces. In this talk, we describe a novel representation of querying for discrete sequential models, along with approximation and search techniques to estimate these probabilistic queries, and touch on extensions to continuous-time events.
About the speaker
Alex Boyd is a Statistics PhD candidate at UC Irvine, co-advised by Padhraic Smyth and Stephan Mandt. His work focuses on improving probabilistic methods, primarily for deep sequential models. He was selected in 2020 as a National Science Foundation Graduate Fellow.