## Past Events › Stochastics and Statistics Seminar Series

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

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## 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 »## Locally private estimation, learning, inference, and optimality

John Duchi (Stanford University)

Abstract: In this talk, we investigate statistical learning and estimation under local privacy constraints, where data providers do not trust the collector of the data and so privatize their data before it is even collected. We identify fundamental tradeoffs between statistical utility and privacy in such local models of privacy, providing instance-specific bounds for private estimation and learning problems by developing local minimax risks. In contrast to approaches based on worst-case (minimax) error, which are conservative, this allows us to…

Find out more »## Efficient Algorithms for the Graph Matching Problem in Correlated Random Graphs

Tselil Schramm (Harvard University)

Abstract: The Graph Matching problem is a robust version of the Graph Isomorphism problem: given two not-necessarily-isomorphic graphs, the goal is to find a permutation of the vertices which maximizes the number of common edges. We study a popular average-case variant; we deviate from the common heuristic strategy and give the first quasi-polynomial time algorithm, where previously only sub-exponential time algorithms were known. Based on joint work with Boaz Barak, Chi-Ning Chou, Zhixian Lei, and Yueqi Sheng. Biography: Tselil Schramm…

Find out more »## Jingbo Liu

MIT

E18-304

Abstract Concentration of measure refers to a collection of tools and results from analysis and probability theory that have been used in many areas of pure and applied mathematics. Arguably, the first data science application of measure concentration (under the name ‘‘blowing-up lemma’’) is the proof of strong converses in multiuser information theory by Ahlswede, G'acs and K"orner in 1976. Since then, measure concentration has found applications in many other information theoretic problems, most notably the converse (impossibility) results in…

Find out more »## Boaz Nadler

Weizmann Institute

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

Find out more »## An Information-Geometric View of Learning in High Dimensions

Gregory Wornell (32-155)

32-155

Abstract: We consider the problem of data feature selection prior to inference task specification, which is central to high-dimensional learning. Introducing natural notions of universality for such problems, we show a local equivalence among them. Our analysis is naturally expressed via information geometry, and represents a conceptually and practically useful learning methodology. The development reveals the key roles of the singular value decomposition, Hirschfeld-Gebelein-Renyi maximal correlation, canonical correlation and principle component analyses, Tishby's information bottleneck, Wyner's common information, Ky Fan…

Find out more »## Dejan Slepcev

Carnegie Mellon University

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

Find out more »## Fitting a putative manifold to noisy data

Hariharan Narayanan (Tata Institute of Fundamental Research, Mumbai)

E18-304

Abstract: We give a solution to the following question from manifold learning. Suppose data belonging to a high dimensional Euclidean space is drawn independently, identically distributed from a measure supported on a low dimensional twice differentiable embedded compact manifold M, and is corrupted by a small amount of i.i.d gaussian noise. How can we produce a manifold $M_o$ whose Hausdorff distance to M is small and whose reach (normal injectivity radius) is not much smaller than the reach of M?…

Find out more »## Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions

Adel Javanmard (USC)

MIT Building E18, Room 304

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

Find out more »## Size-Independent Sample Complexity of Neural Networks

Ohad Shamir (Weizman Institute)

MIT Building E18, Room 304