When Inference is tractable

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.