活动

2022年10月19日

10:00 – 11:30 (UTC+8, HKT)

Webinar

Prof. Ye Luo, Associate Director, Institute of Digital Economy and Innovation, HKU

Prof. Ye Luo is the Associate Director of Institute of Digital Economy and Innovation, HKU. He received his Ph.D from Massachusetts Institute of Technology in year 2015. Dr. Luo’s main research interests include high dimensional econometrics/ statistics, machine learning and its empirical applications in economics and finance. He also has interest and expertise in natural language processing.

For enquiries, please contact us at fssc06@cuhk.edu.hk.

This talk identifies and addresses dynamic selection problems that arise in online learning algorithms with endogenous data. In a contextual multi-armed bandit model,
we show that a novel bias (self-fulfilling bias) arises because the endogeneity of the
data influences the choices of decisions, affecting the distribution of future data to be
collected and analysed.

A class of algorithms to correct for the bias by incorporating instrumental variables into leading online learning algorithms will be proposed. These algorithms lead to the true parameter values and meanwhile atiain low (logarithmic-like) regret levels. I will further prove a central limit theorem for statistical inference of the parameters of interest. To establish the theoretical properties, a general technique that untangles the interdependence between data and actions is developed.