Learning with Less Data: Automated Machine Learning and Bayesian Optimization
In this talk, I will briefly describe our research efforts on learning with less data and automated machine learning. Then, I will discuss in greater detail on one of such efforts: Bayesian optimization (BO), specifically, in how we have progressed from tackling some of the fundamental challenges/issues in BO to applying BO to more complex blackbox optimization problems such as in robotics.
Dr. Bryan Low is an Associate Professor of Computer Science at the National University of Singapore and the Director of AI Research at AI Singapore. He obtained the B.Sc. (Hons.) and M.Sc. degrees in Computer Science from National University of Singapore, Singapore, in 2001 and 2002, respectively, and the Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, Pennsylvania, in 2009. His research interests include probabilistic & automated machine learning, planning under uncertainty, and multi-agent/robot systems. Dr. Low is the recipient of the (1) Andrew P. Sage Best Transactions Paper Award for the best paper published in all 3 of the IEEE Transactions on Systems, Man, and Cybernetics - Parts A, B, and C in 2006; (2) National University of Singapore Overseas Graduate Scholarship for Ph.D. studies in Carnegie Mellon University (CMU) in 2004-2009; (3) Singapore Computer Society Prize for Best M.Sc. Thesis in School of Computing, National University of Singapore in 2003; and (4) Faculty Teaching Excellence Award in School of Computing, National University of Singapore in 2017-2018. Dr. Low has served as a World Economic Forum’s Global Future Councils Fellow for the Council on the Future of Artificial Intelligence and Robotics from Sep 2016 to Jun 2018 and an IEEE Robotics & Automation Society (RAS) Distinguished Lecturer for the IEEE RAS Technical Committee on Multi-Robot Systems in Mar 2019. He has served as an organizing chair for the IEEE RAS Summer School on Multi-Robot Systems in Jun 2016, the AI Summer Schools in Jul 2019 and Aug 2020, and the NeurIPS 2021 Workshop on New Frontiers in Federated Learning. Dr. Low has also served as associate editors, area chairs and program committee members, and reviewers for premier AI (specifically, multiagent systems, AI planning, robotics, machine learning) conferences: IJCAI, AAAI, ECAI, AAMAS, ICAPS, RSS, IROS, ICRA, CoRL, NeurIPS, ICML, AISTATS, ICLR and journals: TKDE, JMLR, JAIR, MLJ, TNNLS, T-ASE, IJRR, T-RO, AURO, JFR, TOSN, JAAMAS. He was the top 5% reviewer for ICML 2019, top 33% reviewer for ICML 2020, and an expert reviewer for ICML 2021.