报告题目：USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems

报告时间：2023年3月2日（星期四）9:00-10:00

报告方式：腾讯会议室：744-8325-9832

报告摘要：Real-world decision-making systems are often subject to uncertainties that have to be resolved through observational data. Therefore, we are frequently confronted with combinatorial optimization problems of which the objective function is unknown and thus has to be debunked using empirical evidence. In contrast to the common practice that relies on a learning-and-optimization strategy, we consider the regression between combinatorial spaces, aiming to infer high-quality optimization solutions from samples of input-solution pairs – without the need to learn the objective function. Our main deliverable is a universal solver that is able to handle abstract undetermined stochastic combinatorial optimization problems. For learning foundations, we present learning-error analysis under the PAC-Bayesian framework using a new margin-based analysis. In empirical studies, we demonstrate our design using proof-of-concept experiments, and compare it with other methods that are potentially applicable. Overall, we obtain highly encouraging experimental results for several classic combinatorial problems on both synthetic and real-world datasets.（该工作发表于NeurIPS2021）

报告人简介：Dr. Guangmo Tong is an Assistant Professor in the Department of Computer and Information Sciences at the University of Delaware. He received a Ph.D. in Computer Science from the University of Texas at Dallas, and a BS degree in Mathematics and Applied Mathematics from Beijing Institute of Technology. His research interests include combinatorial optimization, machine learning, and computational social systems.