BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:John Shawe-Taylor (University College London) DTSTART:20200811T163000Z DTEND:20200811T174500Z DTSTAMP:20260423T021055Z UID:IASML/18 DESCRIPTION:Title: S tatistical Learning Theory for Modern Machine Learning\nby John Shawe- Taylor (University College London) as part of IAS Seminar Series on Theore tical Machine Learning\n\n\nAbstract\nProbably Approximately Correct (PAC) learning has attempted to analyse the generalisation of learning systems within the statistical learning framework. It has been referred to as a ‘worst case’ analysis\, but the tools have been extended to analyse ca ses where benign distributions mean we can still generalise even if worst case bounds suggest we cannot. The talk will cover the PAC-Bayes approach to analysing generalisation that is inspired by Bayesian inference\, but l eads to a different role for the prior and posterior distributions. We wil l discuss its application to Support Vector Machines and Deep Neural Netwo rks\, including the use of distribution defined priors.\n LOCATION:https://researchseminars.org/talk/IASML/18/ END:VEVENT END:VCALENDAR