Multi-objective evolutionary neural architecture search: Communication efficiency, privacy preservation, and adversarial robustness
This talk presents multi-objective approaches to evolutionary neural architecture search in centralized and distributed environments. We introduce multi-objective evolutionary algorithms that aim to enhance computational efficiency and reduce communication cost in privacy-preserving federated neural architecture search. In addition, surrogate-assisted evolutionary search for neural network architectures that are robust to multiple adversarial attacks are presented.
Yaochu Jin is an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, with the Faculty of Technology, Bielefeld University, Germany. He is also a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. He was a “Finland Distinguished Professor” of University of Jyväskylä, Finland, “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include multi-objective and data-driven evolutionary optimization, evolutionary multi-objective learning, trustworthy AI, and evolutionary developmental AI.
Prof Jin is presently the President-Elect of the IEEE Computational Intelligence Society and the Editor-in-Chief of Complex & Intelligent Systems. He was named by the Web of Science as “a Highly Cited Researcher” from 2019 to 2022 consecutively. He is a Member of Academia Europaea and Fellow of IEEE.
金教授目前是IEEE计算智能学会的当选主席，也是《复杂与智能系统》杂志的主编。他于2019年至2022年连续被Web of Science评为“高被引研究员”。他是欧洲科学院的成员和IEEE的研究员。