Sinong Geng FPO

Sinong Geng will present his FPO "Model-Regularized Machine Learning for Decision-Making" on Thursday, April 13, 2023 at 2:30 PM in COS 402 and Zoom.

Location: Zoom link: https://princeton.zoom.us/j/95544518239

The members of Sinong’s committee are as follows:
Examiners: Ronnie Sircar (Adviser), Ryan Adams, Karthik Narasimhan
Readers: Sanjeev Kulkarni, Tom Griffiths

A copy of his thesis is available upon request.  Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis. 
 
Everyone is invited to attend his talk. 
 
Abstract follows below:
Thanks to the availability of more and more high-dimensional data, recent developments in machine learning (ML) have redefined decision-making in numerous domains. However, the battle against the unreliability of ML in decision-making caused by the lack of high-quality data has not ended and is an important obstacle in almost every application. Some questions arise like (i) Why does an ML method fail to replicate the decision-making behaviors in a new environment? (ii) Why does ML give unreasonable interpretations for existing expert decisions? (iii) How to make decisions under a noisy and high-dimensional environment? Many of these issues can be attributed to the lack of an effective and sample-efficient model underlying ML methods.

This thesis presents our research efforts dedicated to developing model-regularized ML for decision-making to address the above issues in areas of inverse reinforcement learning and reinforcement learning, with applications to customer/company behavior analysis and portfolio optimization. Specifically, by applying regularizations derived from suitable models, we propose methods for two different goals: (i) to better understand and replicate existing decision-making of human experts and businesses; (ii) to conduct better sequential decision-making, while overcoming the need for large amounts of high-quality data in situations where there might not be enough.