BEGIN:VCALENDAR VERSION:2.0 PRODID:-// BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:19700329T010000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:19701025T020000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:Statistics SUMMARY:Approximate Inference for the Loss-Calibrated Baye sian - Simon Lacoste-Julien (Cambridge) DTSTART;TZID=Europe/London:20110218T160000 DTEND;TZID=Europe/London:20110218T170000 UID:TALK28560AT URL: DESCRIPTION:Bayesian decision theory provides a well-defined t heoretical framework\nfor rational decision making under uncertainty. However\, even if we\nassume t hat our subjective beliefs about the world have be en\nwell-specified\, we usually need to resort to approximations in order\nto use them in practice. Despite the central role of the loss in the\ndecis ion theory formulation\, most prevalent Bayesian a pproximation\nmethods focus on approximating the p osterior over parameters with no\nconsideration of the loss. In this talk\, our main point is to bri ng\nback in focus the need to *calibrate* the appr oximation methods to the\n*loss* under considerati on. This philosophy has already been widely\nappli ed in the frequentist statistics / discriminative machine\nlearning literature\, as for example with the use of surrogate loss\nfunctions\, but not in Bayesian statistics surprisingly. We provide\nexa mples showing the limitation of disregarding the l oss in standard\napproximate inference schemes and outline several interesting research\ndirections arising from this new perspective. As a first\nlos s-calibrated attempt\, we propose an EM-like algor ithm on the\nBayesian posterior risk and show how it can improve a standard\napproach to Gaussian pr ocess classification when the losses are\nasymmetr ic.\n\n\n LOCATION:MR12\, CMS\, Wilberforce Road\, Cambridge\, CB3 0W B CONTACT:Richard Nickl END:VEVENT END:VCALENDAR