IoT Thrust Seminar | Online Resource Allocation: A Tale of Three Performance Trade-offs
Supporting the below United Nations Sustainable Development Goals:支持以下聯合國可持續發展目標：支持以下联合国可持续发展目标：
Over the past few decades, research interest in online resource allocation—a key area of online optimization—has grown significantly due to its impactful applications in Internet advertising, network routing, financial trading, and ridesharing. Despite extensive research, the main theoretical measure for evaluating online algorithms remains the "worst-case competitive analysis" framework, which compares the algorithm to an offline optimal adversary. Looking ahead, a crucial question in the field is whether there is a more effective way to assess algorithm quality beyond the limits of "worst-case analysis" and "competitive analysis." In this talk, we will examine several classic online resource allocation problems, such as online conversion, online selection, and online assignment, and introduce new metrics for understanding various performance trade-offs in online resource allocation. We will particularly focus on balancing traditional objectives like social welfare with other important metrics, including simplicity, fairness, and risk, etc. Ultimately, our goal is to inspire new approaches to evaluating and designing online algorithms that better meet practical needs and diverse real-world scenarios.
Xiaoqi Tan is an Assistant Professor in the Department of Computing Science at the University of Alberta and a Fellow of Alberta Machine Intelligence Institute (Amii). Prior to July 2021, he was a Postdoctoral Fellow at the University of Toronto. He received his Ph.D. from HKUST in 2018. During his Ph.D., he was also a visiting research fellow at the School of Engineering and Applied Science, Harvard University. Xiaoqi’s research spans various topics in optimization and decision-making under uncertainty. He develops new methodologies and uses existing ones from online optimization, algorithmic game theory, mechanism design, and machine learning. On the practical side, his research is motivated from and applied to societal challenges across various fields, including energy, transportation, online advertising, financial trading, cloud computing, and network optimization.