Abstract: A key capability of artificial intelligence will be the ability to reason about abstract concepts and draw inferences. Where data is limited, probabilistic inference in graphical models provides a powerful framework for performing such reasoning, and can even be used as modules within deep architectures. But, when is probabilistic inference computationally tractable? I will present recent theoretical results that substantially broaden the class of provably tractable models by exploiting model stability (Lang, Sontag, Vijayaraghavan, AI Stats ’18), structure in model parameters (Weller, Rowland, Sontag, AI Stats ’16), and reinterpreting inference as ground truth recovery (Globerson, Roughgarden, Sontag, Yildirim, ICML ’15).
Biography: David Sontag is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, and member of the Institute for Medical Engineering and Science and the Computer Science and Artificial Intelligence Laboratory (CSAIL). Prior to joining MIT, Dr. Sontag was an Assistant Professor in Computer Science and Data Science at New York University from 2011 to 2016, and a postdoctoral researcher at Microsoft Research New England. Dr. Sontag received the Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NIPS), faculty awards from Google, Facebook, and Adobe, and a National Science Foundation Early Career Award. Dr. Sontag received a B.A. from the University of California, Berkeley.
A video of the seminar is available to watch here.