Year: 2024
Type: article
Abstract: Sequential decision‐making involves making informed decisions based on continuous interactions with a complex environment. This process is ubiquitous in various applications, including recommendation systems and clinical treatment design. My research has concentrated on addressing two pivotal challenges in sequential decision‐making: (1) How can we design algorithms that efficiently learn the optimal decision strategy with minimal interactions and limited sample data? (2) How can we ensure robustness in decision‐making algorithms when faced with distributional shifts due to environmental changes and the sim‐to‐real gap? This paper summarizes and expands upon the talk I presented at the AAAI 2024 New Faculty Highlights program, detailing how my research aims to tackle these challenges.
Source: AI Magazine
DOI: 10.1002/aaai.12186
Author: Pan Xu
Institution: Duke University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,400
Funder: National Science Foundation
OpenAlex ID: W4402723313