Colloquia

Spring 2020 Colloquia

Upcoming Colloquia:

  • Friday, January 10th: Jiwei Zhao, State University of New York at Buffalo - Colloquium @ 10am in 214 Duxbury Hall
  • Monday, January 13th: Yuqi Gu, University of Michigan - Colloquium @ 10am in 499 Dirac Science Library
  • Wednesday, January 15th: Rongjie Liu, Rice University - Colloquium @ 10am in 499 Dirac Science Library
  • Friday, January 17th: Jonathan Stewart, Rice University - Colloquium @ 10am in 214 Duxbury Hall
  • Wednesday, January 22nd: Wenjia Wang, Duke University - Colloquium @ 10am in 499 Dirac Science Library
  • Friday, January 24th: Nicholas Syring, St. Louis University - Colloquium @ 10am in 214 Duxbury Hall
  • Monday, January 27th: Ray Bai, University of Pennsylvania - Colloquium @ 10am in 499 Dirac Science Library
  • Friday, January 31st: Hyebin Song, University of Wisconsin - Colloquium @ 10am in 214 Duxbury Hall
  • Monday, February 3rd: Tianjian Zhou, University of Chicago - Colloquium @ 10am in 499 Dirac Science Library
     

Previous Colloquia:

Wednesday, January 8th: Fangzheng Xie, Johns Hopkins University

499 Dirac Science Library, 10:00am

Title: Global and Local Estimation of Low-Rank Random Graphs

Abstract: Random graph models have been a heated topic in statistics and machine learning, as well as a broad range of application areas. In this talk I will give two perspectives on the estimation task of low-rank random graphs. Specifically, I will focus on estimating the latent positions in random dot product graphs. The first component of the talk focuses on the global estimation task. The minimax lower bound for global estimation of the latent positions is established, and this minimax lower bound is achieved by a Bayes procedure, referred to as the posterior spectral embedding. The second component of the talk addresses the local estimation task. We define local efficiency in estimating each individual latent position, propose a novel one-step estimator that takes advantage of the curvature information of the likelihood function (i.e., derivatives information) of the graph model, and show that this estimator is locally efficient. The previously widely adopted adjacency spectral embedding is proven to be locally inefficient due to the ignorance of the curvature information of the likelihood function. Simulation examples and the analysis of a real-world Wikipedia graph dataset are provided to demonstrate the usefulness of the proposed methods.

 

Previous Colloquia

Fall 2019 Colloquia

Spring 2019 Colloquia

Fall 2018 Colloquia

Spring 2018 Colloquia

Fall 2017 Colloquia

Spring 2016 Colloquia Part II

Fall 2016 Colloquia

Spring 2016 Colloquia

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