BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Eric Xing (Carnegie Mellon University) DTSTART:20200806T190000Z DTEND:20200806T203000Z DTSTAMP:20260423T021057Z UID:IASML/17 DESCRIPTION:Title: A Blueprint of Standardized and Composable Machine Learning\nby Eric Xi ng (Carnegie Mellon University) as part of IAS Seminar Series on Theoretic al Machine Learning\n\n\nAbstract\nIn handling wide range of experiences r anging from data instances\, knowledge\, constraints\, to rewards\, advers aries\, and lifelong interplay in an ever-growing spectrum of tasks\, cont emporary ML/AI research has resulted in thousands of models\, learning par adigms\, 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 ma ke a comprehensive grasp of existing ML techniques more and more difficult \, and make standardized\, reusable\, repeatable\, reliable\, and explaina ble practice and further development of ML/AI products quite costly\, if p ossible\, at all. In this talk\, we present a simple and systematic bluepr int of ML\, from the aspects of losses\, optimization solvers\, and model architectures\, that provides a unified mathematical formulation for learn ing with all experiences and tasks. The blueprint offers a holistic unders tanding of the diverse ML algorithms\, guidance of operationalizing ML for creating problem solutions in a composable and mechanic manner\, and unif ied framework for theoretical analysis.\n LOCATION:https://researchseminars.org/talk/IASML/17/ END:VEVENT END:VCALENDAR