Model-Regularized Machine Learning for Decision-Making

Abstract

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 should we 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.