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Friday, May 13, 2022

Time: 01:45 - 02:45 PM ET
Recording of this Online Seminar on Youtube

Eric Xing -- From Learning, to Meta-Learning, to "Lego-Learning" -- theory, system, and engineering

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Abstract: Software systems for complex tasks - such as controlling manufacturing processes in real-time; or writing radiological case reports within a clinical workflow — are becoming increasingly sophisticated and consist of a large number of data, model, algorithm, and system elements and modules. Traditional benchmark/leaderboard-driven bespoke approaches in the Machine Learning community are not suited to meet the highly demanding industrial standards beyond algorithmic performance, such as cost-effectiveness, safety, scalability, and automatability, typically expected in production systems. In this talk, I discuss some technical issues toward addressing these challenges: 1) a theoretical framework for panoramic learning with all experiences; 2) optimization methods to best the effort for learning under such a principled framework; 3) compositional strategies for building production-grade ML programs from standard parts. I will present our recent work toward developing a standard model for Learning that unifies different special-purpose machine learning paradigms and algorithms, then a Bayesian blackbox optimization approach to Meta Learning in the space of hyperparameters, model architectures, and system configurations, and finally principles and designs of standardized software Legos that facilitate cost-effective building, training, and tunning of practical ML pipelines and systems.

Bio: Eric P. Xing is the President of the Mohamed bin Zayed University of Artificial Intelligence, a Professor of Computer Science at Carnegie Mellon University, and the Founder and Chairman of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his PhD in Computer Science at UC Berkeley. His main research interests are the development of machine learning and statistical methodology; and composable, automatic, and scalable computational systems, for solving problems involving automated learning, reasoning, and decision-making in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), and IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI); action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); he is a board member of the International Machine Learning Society.