Timezone: »

Collective Model Fusion for Multiple Black-Box Experts
Minh Hoang · Nghia Hoang · Bryan Kian Hsiang Low · Carleton Kingsford

Wed Jun 12 11:30 AM -- 11:35 AM (PDT) @ Room 103

Model fusion is a fundamental problem in collective machine learning (ML) where independent experts with heterogeneous learning architectures are required to combine expertise to improve predictive performance. This is particularly challenging in information-sensitive domains (e.g., medical records in health-care analytics) where experts do not have access to each other's internal architecture and local data. To address this challenge, this paper presents the first collective model fusion framework for multiple experts with heterogeneous black-box architectures. The proposed method will enable this by addressing the following key issues of how black-box experts interact to understand the predictive behaviors of one another; how these understandings can be represented and shared efficiently among themselves; and how the shared understandings can be combined to generate high-quality consensus prediction. The performance of the resulting framework is analyzed theoretically and demonstrated empirically on several datasets.

Author Information

Minh Hoang (Carnegie Mellon University)
Nghia Hoang (MIT-IBM Watson AI Lab, IBM Research)
Bryan Kian Hsiang Low (National University of Singapore)

Dr. Bryan Low is an Associate Professor of Computer Science at the National University of Singapore and the Deputy 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 and the AI Summer Schools in Jul 2019 and Aug 2020. 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 and top 33% reviewer for ICML 2020.

Carleton Kingsford (Carnegie Mellon University)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors