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Seminar on Theoretical Machine Learning

A Blueprint of Standardized and Composable Machine Learning

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 also make a comprehensive grasp of existing ML techniques more and more difficult, and make standardized, reusable, repeatable, reliable, and explainable practice and further development of ML/AI products quite costly, if possible, at all. 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 for learning with all experiences and tasks. The blueprint offers a holistic understanding of the diverse ML algorithms, guidance of operationalizing ML for creating problem solutions in a composable and mechanic manner, and unified framework for theoretical analysis.

Featuring

Eric Xing

Speaker Affiliation

Carnegie Mellon University

Affiliation

Mathematics

Event Series

Notes

We welcome broad participation in our seminar series. To receive login details, interested participants will need to fill out a registration form accessible from the link below.  Upcoming seminars in this series can be found here.

Register Here

Date & Time
August 06, 2020 | 3:004:30pm

Location

Remote Access Only - see link below

Categories