Statistics Department Seminar Series: Eric Xing, Professor of Machine Learning, Language Technology Institute & Computer Science Department School of Computer Science Department, Carnegie Mellon University

Abstract:

In handling wide range of experiences ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interplay, in an ever-growing spectrum of tasks, contemporary ML/AI research has resulted in thousands of models, learning paradigms, optimization algorithms, not mentioning countless approximation heuristics, tuning tricks, and black-box oracles, plus combinations of all above. While pushing the field forward rapidly, these results make a comprehensive grasp of existing ML techniques more and more difficult, and make standardized, reusable, repeatable, and explainable practice and further development of ML/AI solutions costly, if possible. In this talk, we present a simple and systematic blueprint of ML, from the aspects of losses, optimization solvers, and model architectures, that provides a unified mathematical formulation, or a “standard equation” (SE), for learning with all experiences and tasks. SE offers a holistic understanding of the diverse ML algorithms and their connections, potentially facilitates easier operationalization of ML in a composable and mechanic manner; and unified framework for theoretical analysis of all such methods.


Bio:

Eric P. Xing is 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 large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in artificial, biological, and social systems. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department.


This seminar will be livestreamed via Zoom https://umich.zoom.us/j/94350208889
There will be a virtual reception to follow.