Past Probability Seminars Spring 2020: Difference between revisions
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== Thursday, January 15, [http://www.stat.berkeley.edu/~racz/ Miklos Racz], [http://statistics.berkeley.edu/ UC-Berkeley Stats] == | == Thursday, January 15, [http://www.stat.berkeley.edu/~racz/ Miklos Racz], [http://statistics.berkeley.edu/ UC-Berkeley Stats] == | ||
Title: Testing for high-dimensional geometry in random graphs | |||
Abstract: I will talk about a random geometric graph model, where connections between vertices depend on distances between latent d-dimensional labels; we are particularly interested in the high-dimensional case when d is large. Upon observing a graph, we want to tell if it was generated from this geometric model, or from an Erdos-Renyi random graph. We show that there exists a computationally efficient procedure to do this which is almost optimal (in an information-theoretic sense). The key insight is based on a new statistic which we call "signed triangles". To prove optimality we use a bound on the total variation distance between Wishart matrices and the Gaussian Orthogonal Ensemble. This is joint work with Sebastien Bubeck, Jian Ding, and Ronen Eldan. | |||
== Thursday, January 22, TBA == | == Thursday, January 22, TBA == | ||
Revision as of 15:09, 5 January 2015
Spring 2015
Thursdays in 901 Van Vleck Hall at 2:25 PM, unless otherwise noted.
If you would like to sign up for the email list to receive seminar announcements then please send an email to join-probsem@lists.wisc.edu.
Thursday, January 15, Miklos Racz, UC-Berkeley Stats
Title: Testing for high-dimensional geometry in random graphs
Abstract: I will talk about a random geometric graph model, where connections between vertices depend on distances between latent d-dimensional labels; we are particularly interested in the high-dimensional case when d is large. Upon observing a graph, we want to tell if it was generated from this geometric model, or from an Erdos-Renyi random graph. We show that there exists a computationally efficient procedure to do this which is almost optimal (in an information-theoretic sense). The key insight is based on a new statistic which we call "signed triangles". To prove optimality we use a bound on the total variation distance between Wishart matrices and the Gaussian Orthogonal Ensemble. This is joint work with Sebastien Bubeck, Jian Ding, and Ronen Eldan.
Thursday, January 22, TBA
Title: TBA
Abstract:
Thursday, January 29, Arnab Sen, University of Minnesota
Title: TBA
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Thursday, February 5, TBA
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Thursday, February 12, TBA
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Thursday, February 19, TBA
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Thursday, February 26, Dan Crisan, Imperial College London
Title: TBA
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Thursday, March 5, TBA
Title: TBA
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Thursday, March 12, TBA
Title: TBA
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Thursday, March 19, TBA
Title: TBA
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Thursday, March 26, TBA
Title: TBA
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