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  • MASCLE Machine Learning Seminar: David Sontag (MIT) - When Inference is Tractable

    Tue, Jan 30, 2018 @ 04:00 PM - 05:20 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: David Sontag, MIT

    Talk Title: When Inference is Tractable

    Series: Visa Research Machine Learning Seminar Series hosted by USC Machine Learning Center

    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).

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

    Biography: David Sontag joined MIT in January 2017 as Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) and Hermann L. F. von Helmholtz Career Development Professor in the Institute for Medical Engineering and Science (IMES). He is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Sontag's research focuses on machine learning and artificial intelligence; at IMES, he leads a research group that aims to use machine learning to transform health care.

    Previously, he was an assistant professor in computer science and data science at New York University's Courant Institute of Mathematical Sciences 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 NSF CAREER Award. Dr. Sontag received a B.A. from the University of California, Berkeley.

    Host: Yan Liu

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Contact: Computer Science Department


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