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酷游ku111备用线路:南京大学钱超副教授学术报告

来源: 点击: 时间:2022年10月24日 16:39

报告人:钱超

 

报告地点:腾讯会议560-900-361

 

报告时间:20221026日星期三下午3:00

 

报告题目:Multi-objective Evolutionary Learning: Advances in Theories and Algorithms

 

报告简介:

Machine learning tasks often involve multiple objective functions, which may make conventional optimization algorithms fail. Evolutionary algorithms (EAs), inspired by natural evolution, have yielded encouraging outcomes for solving multi-objective optimization problems in machine learning. However, due to the heuristic nature of EAs, most outcomes to date have been empirical and lack theoretical support. In this talk, I will introduce our efforts towards building the theoretical foundation of multi-objective evolutionary learning. First, I will introduce a general theoretical tool for analyzing multi-objective EAs. Based on this tool, I will then present the influence of recombination operators on the performance of multi-objective EAs, and the theoretical findings on how to deal with constraints and noise. Finally, I will introduce multi-objective evolutionary learning algorithms with provable approximation guarantees, inspired by the theoretical results, for two representative learning tasks, selective ensemble and subset selection.

 

 

报告人简介:

钱超,南京大学人工智能学院副教授、博导。分别于2009年和2015年获南京大学计算机系学士和博士学位,之后加入中科大酷游ku111备用线路担任副研究员,2019年回到母校工作。目前主要研究演化计算与演化学习,出版专著《Evolutionary Learning》,并以第一/通讯作者在国际一流期刊和会议(AIJ、TEvC、ECJ、Algorithmica、TCS、AAAI、IJCAI、ICLR、NeurIPS)上发表三十余篇论文,部分成果成功应用于华为工厂排产、无线网络优化等项目,落地华为生产系统。担任IEEE计算智能学会Theoretical Foundations of Bio-inspired Computation工作组主席、IEEE演化计算技术委员会委员、IEEE Trans. on Evolutionary Computation副编、Theoretical Computer Science客座编辑,在国际人工智能联合大会IJCAI’22Early Career Spotlight报告。获ACM GECCO’11最佳理论论文奖、IDEAL’16 最佳论文奖、IEEE CEC’21最佳学生论文奖提名,博士论文获中国人工智能学会优博,并入选中国科协青年人才托举工程(2016),获国家优秀青年科学基金(2020)。

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