THESIS DEFENSE: Scalable Structure Learning and Inference in Probabilistic Programs

Speaker: Feras Saad , MIT EECS

Date: Tuesday, June 21, 2022

Time: 3:00 PM to 4:30 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: Seminar Room D463 (Star)

Event Type: Thesis Defense

Room Description:

Host: Vikash Mansinghka, MIT BCS/CSAIL

Contact: Amanda Brower, 617-253-0093, abrower@mit.edu

Relevant URL: https://mit.zoom.us/j/96924616323

Speaker URL: http://fsaad.mit.edu

Speaker Photo:
None

Reminders to: seminars@csail.mit.edu

Reminder Subject: TALK: Thesis Defense: Scalable Structure Learning and Inference in Probabilistic Programs

Abstract: Probabilistic programming supports probabilistic modeling, learning, and inference by representing sophisticated probabilistic models as computer programs in new programming languages. This thesis presents efficient probabilistic programming-based techniques that address two fundamental challenges in scaling and automating structure learning and inference over complex data. First, I will describe scalable structure learning methods that make it possible to automatically synthesize probabilistic programs in an online setting by performing Bayesian inference over hierarchies of flexibly structured symbolic program representations, for discovering models of time series data, tabular data, and relational data. Second, I will present fast compilers and symbolic analyses that compute exact answers to a broad range of inference queries about these learned programs, which lets us extract interpretable patterns and make accurate predictions in real time.

I will demonstrate how these techniques deliver state-of-the-art performance in terms of runtime, accuracy, robustness, and programmability by drawing on several examples from real-world applications, which include adapting to extreme novelty in economic time series, online forecasting of flu rates given sparse multivariate observations, discovering stochastic motion models of zebrafish hunting, and verifying the fairness of machine learning classifiers.

Thesis Committee: Vikash Mansinghka, Martin Rinard, Armando Solar-Lezama, Joshua Tenenbaum

Research Areas:
AI & Machine Learning, Programming Languages & Software

Impact Areas:

This event is not part of a series.

Created by Feras A. Saad at Monday, June 06, 2022 at 2:34 PM.