Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Ji Zhu

Fast Network Community Detection with Profile-Pseudo Likelihood Methods The stochastic block model is one of the most studied network models for community detection. It is known that most algorithms proposed for fitting the stochastic block model likelihood function cannot scale to large-scale networks. One prominent work that overcomes this computational challenge is Amini et al. (2013), which proposed a fast pseudo-likelihood approach for…


Alexander Aue

Random matrix theory aids statistical inference in high dimensions This talk is on bootstrapping spectral statistics in high dimensions. Spectral statistics play a central role in many multivariate testing problems. It is therefore of interest to approximate the distribution of functions of the eigenvalues of sample covariance matrices. Although bootstrap methods are an established approach to approximating the laws of spectral statistics in low…


Linglong Kong

Statistics and Optimization in Reinforcement Learning Reinforcement Learning (RL) is a mathematical framework to develop intelligent agents that can learn the optimal behaviour that maximizes the cumulative reward by interacting with the environment. There are numerous successful applications in many fields. Statistics and optimization are becoming important tools for RL. In this talk, we will look at two of our recent developments. In the first…


Yuan Liao


Zhana Duren


Yanqing Wang


Yuefeng Han


Lan Wang


Jun S Liu


Runmin Wang

Subscribe to Colloquium Series