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## Topics in Information and Inference Seminar

Guy Bresler (MIT)

This seminar consists of a series of lectures each followed by a period of informal discussion and social. The topics are at the nexus of information theory, inference, causality, estimation, and non-convex optimization. The lectures are intended to be tutorial in nature with the goal of learning about interesting and exciting topics rather than merely hearing about the most recent results. The topics are driven by the interests of the speakers, and with the exception of the two lectures on…

Find out more »## Algorithmic thresholds for tensor principle component analysis

Aukosh Jagannath (Harvard University)

Abstract: Consider the problem of recovering a rank 1 tensor of order k that has been subject to Gaussian noise. The log-likelihood for this problem is highly non-convex. It is information theoretically possible to recover the tensor with a finite number of samples via maximum likelihood estimation, however, it is expected that one needs a polynomially diverging number of samples to efficiently recover it. What is the cause of this large statistical–to–algorithmic gap? To study this question, we investigate the…

Find out more »## Topics in Information and Inference Seminar

Abbas El Gamal (Stanford University)

This seminar consists of a series of lectures each followed by a period of informal discussion and social. The topics are at the nexus of information theory, inference, causality, estimation, and non-convex optimization. The lectures are intended to be tutorial in nature with the goal of learning about interesting and exciting topics rather than merely hearing about the most recent results. The topics are driven by the interests of the speakers, and with the exception of the two lectures on…

Find out more »## On the cover time of two classes of graph

Alan Frieze (Carnegie Mellon University)

Abstract: Dense Graphs: We consider abritrary graphs G with n vertices and minimum degree at least n. where δ > 0 is constant. If the conductance of G is suﬃciently large then we obtain an asymptotic expression for the cover time CG of G as the solution to some explicit transcendental equation. Failing this, if the mixing time of a random walk on G is of a lesser magnitude than the cover time, then we can obtain an asymptotic deterministic…

Find out more »## Topics in Information and Inference Seminar

Abbas El Gamal (Stanford University)

This seminar consists of a series of lectures each followed by a period of informal discussion and social. The topics are at the nexus of information theory, inference, causality, estimation, and non-convex optimization. The lectures are intended to be tutorial in nature with the goal of learning about interesting and exciting topics rather than merely hearing about the most recent results. The topics are driven by the interests of the speakers, and with the exception of the two lectures on…

Find out more »## Stochastics and Statistics Seminar

Sumit Mukherjee (Columbia University)

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

Find out more »## Topics in Information and Inference Seminar

Suvrit Sra (MIT)

## Stochastics and Statistics Seminar

Zongming Ma (University of Pennsylvania)

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

Find out more »## Topics in Information and Inference Seminar

Devavrat Shah (MIT)

## Stochastics and Statistics Seminar

Lucas Janson (Harvard University)

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

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