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TAP free energy, spin glasses, and variational inference
Zhou Fan (Yale University)
E18-304
Abstract: We consider the Sherrington-Kirkpatrick model of spin glasses with ferromagnetically biased couplings. For a specific choice of the couplings mean, the resulting Gibbs measure is equivalent to the Bayesian posterior for a high-dimensional estimation problem known as "Z2 synchronization". Statistical physics suggests to compute the expectation with respect to this Gibbs measure (the posterior mean in the synchronization problem), by minimizing the so-called Thouless-Anderson-Palmer (TAP) free energy, instead of the mean field (MF) free energy. We prove that this identification…
Find out more »Medical Image Imputation
Polina Golland (MIT CSAIL)
E18-304
Abstract: We present an algorithm for creating high resolution anatomically plausible images that are consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images. Our goal is to enable application of existing algorithms that were originally…
Find out more »Collective Decision Making: Theory and Experiments
Leeat Yariv (Princeton University)
32-155
Abstract: Ranging from jury decisions to political elections, situations in which groups of individuals determine a collective outcome are ubiquitous. There are two important observations that pertain to almost all collective processes observed in reality. First, decisions are commonly preceded by some form of communication among individual decision makers, such as jury deliberations, or election polls. Second, even when looking at a particular context, say U.S. civil jurisdiction, there is great variance in the type of institutions that are employed…
Find out more »Data Science and Big Data Analytics: Making Data-Driven Decisions
Developed by 11 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins Feb 4, 2019.
Find out more »Optimization of the Sherrington-Kirkpatrick Hamiltonian
Andrea Montanari (Stanford University )
E18-304
Andrea Montanari Professor, Department of Electrical Engineering, Department of Statistics Stanford University This lecture is in conjunction with the LIDS Student Conference. Abstract: Let A be n × n symmetric random matrix with independent and identically distributed Gaussian entries above the diagonal. We consider the problem of maximizing xT Ax over binary vectors with ±1 entries. In the language of statistical physics, this amounts to finding the ground state of the Sherrington-Kirkpatrick model of spin glasses. The asymptotic value of this…
Find out more »Laboratory for Information & Decision Systems (LIDS) Student Conference
The annual LIDS Student Conference is a student-organized, student-run event that provides an opportunity for grad students to present their research to peers as well as to the community at large.
Find out more »Large girth approximate Steiner triple systems
Lutz Warnke (Georgia Institute of Technology)
E18-304
Abstract: In 1973 Erdos asked whether there are n-vertex partial Steiner triple systems with arbitrary high girth and quadratically many triples. (Here girth is defined as the smallest integer g \ge 4 for which some g-element vertex-set contains at least g-2 triples.) We answer this question, by showing existence of approximate Steiner triple systems with arbitrary high girth. More concretely, for any fixed \ell \ge 4 we show that a natural constrained random process typically produces a partial Steiner triple…
Find out more »SES PhD Admissions Info Session
Ali Jadbabaie (MIT)
E18-411
Learn about admission to the Social and Engineering Systems Doctoral Program. Info session is hosted by a member of the IDSS faculty and an SES student, who introduce the program and answer your questions. See the flier or our website for more information.
Find out more »Naomi E. Leonard
Naomi E. Leonard (Princeton University)
32-155
Title Talk: Symmetry, Bifurcation, and Multi-Agent Decision-Making Speaker: Naomi E. Leonard Affiliation: Princeton University Abstract: Prof. Leonard will present nonlinear dynamics for distributed decision-making that derive from principles of symmetry and bifurcation. Inspired by studies of animal groups, including house-hunting honeybees and schooling fish, the nonlinear dynamics describe a group of interacting agents that can manage flexibility as well as stability in response to a changing environment. Bio: Prof. Naomi Ehrich Leonard is Edwin S. Wilsey Professor of Mechanical and…
Find out more »Reducibility and Computational Lower Bounds for Some High-dimensional Statistics Problems
Guy Bresler (MIT)
E18-304
Abstract: The prototypical high-dimensional statistics problem entails finding a structured signal in noise. Many of these problems exhibit an intriguing phenomenon: the amount of data needed by all known computationally efficient algorithms far exceeds what is needed for inefficient algorithms that search over all possible structures. A line of work initiated by Berthet and Rigollet in 2013 has aimed to explain these gaps by reducing from conjecturally hard problems in computer science. However, the delicate nature of average-case reductions has…
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