题目：Many inequalities selection via Machine Learning

主讲人：罗晔，香港大学

时间：2019年11月15日，13:30-15:00

地点：暨南大学中惠楼106B室

主讲人简介:

Dr. Ye Luo received his Ph.D from Masschusetts Institute of Technology at year 2015. He received his B.S. degree from Massachusetts Institute of Technology at year 2010, majored in Mathematics and Economics. Before joining FBE of HKU, he worked as assistant professor at the economics department in University of Florida. Dr. Ye Luo's main research interests include high dimensional econometrics/statistics, machine learning and its empirical applications in economics and finance, for example, applying AI algorithms to develop smart, adaptive automated trading systems, applying big data methods/machine learning in default risk prediction, dynamic demand prediction, etc. He also has interest and expertise in natural language processing.

Dr. Ye Luo has research papers published/forthcoming at Econometrica, Journal of the Royal Statistical Society: Series B, American Economic Review, P&P, etc. Beyond Dr. Ye Luo's academic research, he has a strong interest in connecting the research in data science to the industry. He has given/being invited to give lectures at DiDi, ShunFeng Express, Novartis, etc.

Abstract:

Many economics and operations research problems generate a large set of linear inequalities constraints. The large number of constraints can make optimization and inference problem time costly. We propose a machine learning method, similar to the Dantzig Selector, based on relaxation of Farkas lemma. We prove the asymptotic properties of our selection method, and demonstrate the effectiveness of such selection process by simulation, compare to other more standard methods..