Guanhua Fang (Baidu USA)
DateWednesday March 16, 2022
10:00 am - 11:00 am
LocationOnline via Zoom
Wednesday, March 16th, 2022
Time: 10:00 a.m. Place: Online via Zoom (contact Brian Ling for Zoom link)
Speaker: Guanhua Fang (Baidu USA)
Title: Advances in Machine Learning with Heavy-tailed Distributions
Abstract: In many fields such as telecommunications, survival analysis, quality control, online recommendation, and reinforcement learning, one often encounters situations where the data sources behave normally most of the time, but sometimes could become hetero genous and unstructured. Learning in these applications should consider reward distributions with tails heavier than the normal distribution.
In the literature, a remarkable M-estimator proposed by Catoni (2012) has been shown to be rate-optimal in mean estimation problems with finite variance condition. During this talk, I will discuss more advanced statistical learning results based on Catoni-type estimators especially in the situations with infinite variance or presence of contaminated observations. Several interesting applications are given to show how new theory can be adapted into machine learning tasks to achieve better performance.