SPEAKER: David Gamarnik, Professor of Operations Research, MIT Sloan School of Management
TITLE: "Algorithmic Challenges in High-Dimensional Inference Models. Insights from the Statistical Physics"
ABSTRACT: Inference problems arising in modern day statistics, machine learning and artificial intelligence fields often involve models with exploding dimensions, giving rise to a multitude of computational challenges. Many such problems "infamously" resist the construction of tractable inference algorithms, and thus are possibly fundamentally non-solvable by fast computational methods. A particularly intriguing form of such intractability is the so-called computational vs information theoretic gap, where effective inference is achievable by some form of exhaustive search type computational procedure, but fast computational methods are not known and conjectured not to exist. A great deal of insight into the mysterious nature of this gap has emerged from the field of statistical physics, where the computational difficulty is linked to a phase transition phenomena of the solution space topology. We will discuss one such phase transition obstruction, which takes the form of the Overlap Gap Property: the property referring to the topological disconnectivity (gaps) of the set of valid solutions.
SPEAKER WEBSITE: (http://www.mit.edu/~gamarnik/home.html)
This is a joint Statistics-MADDD Seminar. MADDD is the "Mathematics of Data and Decisions at Davis" seminar series hosted by the Math Department. For more info please visit: https://sites.google.com/view/maddd
DATE: Tuesday, March 3rd, 4:10pm
LOCATION: MSB 1147, Colloquium Room
REFRESHMENTS at 3:30pm in MSB 1147