Speaker: Yuejiao Gong, South China University of Technology, China
Date: 12 August 2024
Time: 4:00 - 5:00pm (Beijing Time, UTC+8) [Convert to your local time]
Zoom link: https://vuw.zoom.us/j/96570642754
Yue-Jiao Gong is a Professor at the School of Computer Science and Engineering, South China University of Technology, China. Her research interests include Optimization Methods based on Swarm Intelligence, Deep Learning, Reinforcement Learning, and their Applications in Smart Cities and Intelligent Transportation. She has published over 100 papers, primarily in ACM\IEEE Transactions series journals and reputable conferences such as GECCO, NeurIPS, and ICLR. Dr. Gong was honored as the Pearl River Young Scholar by the Guangdong Education Department in 2017 and received the Guangdong Natural Science Funds for Distinguished Young Scholars in 2022.
Meta-Black-Box Optimization (MetaBBO) leverages a meta-level learner to automate the black-box optimization process, thereby enhancing efficiency and reducing human intervention. MetaBBO can be categorized into three distinct branches: the first focuses on automatic algorithm configuration or selection; the second employs neural networks to propose candidate solutions directly; and the third generates algorithms, as demonstrated in our study, by producing update rules expressed as closed-form equations. This talk provides a comprehensive overview of these three branches of MetaBBO, highlighting their methodologies, advantages, and current challenges.
Speaker: Su Nguyen, RMIT University, Australia
Date: 2 December 2024
Time: TBA
Zoom link: TBA
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Speaker: Junchi Yan, Shanghai Jiao Tong University, China
Date: 13 January 2025
Time: TBA
Zoom link: TBA
Junchi Yan is currently a Full Professor with School of Artificial Intelligence, Shanghai Jiao Tong University (SJTU), Shanghai, China. Before that, he was a Senior Research Staff Member with IBM Research China during 2011-2018. He received his PhD in Electronic Engineering from SJTU in 2015. He serves as Associate Editor for IEEE TPAMI, Pattern Recognition Journal, and Area Chair for ICML, ICLR, NeurIPS etc. He has received major fundings in AI from NSFC and MOST up to 10M RMB. His main research interest is machine learning and its intersection with other areas especially operations research. He has received the award of CVPR 2024 best paper candidate, the most influential papre by PaperDigest (NO.1) of AAAI21 and IJCAI23. He also received the People of the year for Intelligent Computing in 2023 by MIT Tech Review. He is a Fellow of IAPR and Senior Member of IEEE.
In this talk, I will introduce our works on developing machine learning models for solving different problems in combinatorial optimization, regarding with both AI native and AI aided approaches, which are developing very fast especially in the machine learning community. Moreover, I will also cover the emerging applications including those from electronic design automation, computational chemistry and quantum computing. I will conclude the talk with some fresh thoughts on our ongoing and future works, and the potential of LLM and other kinds of (so-called) foundation models for combinatorial optimization.
Speaker: Meng Xu, A*STAR - Agency for Science, Technology and Research, Singapore
Date: 17 February 2025
Time: TBA
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Speaker: Handing Wang, Xidian University, China
Date: TBA 2025
Time: TBA
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If you have any questions or queries, please email Yi Mei or Fangfang Zhang.
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