BEGIN:VCALENDAR VERSION:2.0 PRODID:-//https://caida.ubc.ca//NONSGML iCalcreator 2.41.92// CALSCALE:GREGORIAN METHOD:PUBLISH UID:32326266-6238-4566-b938-323734613534 X-WR-RELCALID:efc09d74-9c93-479e-a94f-485231ddccde X-WR-TIMEZONE:America/Vancouver X-WR-CALNAME:LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language - James Requeima\, Postdoctoral Fellow\, University of Toronto BEGIN:VTIMEZONE TZID:America/Vancouver TZUNTIL:20261101T090000Z BEGIN:STANDARD TZNAME:PST DTSTART:20241103T020000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RDATE:20251102T020000 END:STANDARD BEGIN:DAYLIGHT TZNAME:PDT DTSTART:20240310T020000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RDATE:20250309T020000 RDATE:20260308T020000 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:34476516-1d1f-45b3-89f8-5a5bf0c463b2 DTSTAMP:20260227T085153Z CLASS:PUBLIC CREATED:20241210T235242Z DESCRIPTION:Abstract: Machine learning practitioners often face significant challenges in formally integrating their prior knowledge and beliefs into predictive models\, limiting the potential for nuanced and context-aware analyses. Moreover\, the expertise needed to integrate this prior knowledg e into probabilistic modeling typically limits the application of these mo dels to specialists. In this talk\, we present a regression model that can process numerical data and make probabilistic predictions at arbitrary lo cations\, guided by natural language text which describes a user's prior k nowledge. Large… DTSTART;TZID=America/Vancouver:20241216T104500 DTEND;TZID=America/Vancouver:20241216T114500 LAST-MODIFIED:20241211T000005Z LOCATION:UBC Vancouver Campus\, ICCS X836 SUMMARY:LLM Processes: Numerical Predictive Distributions Conditioned on Na tural Language - James Requeima\, Postdoctoral Fellow\, University of Toro nto TRANSP:OPAQUE URL:https://caida.ubc.ca/event/llm-processes-numerical-predictive-distribut ions-conditioned-natural-language-james-requeima END:VEVENT END:VCALENDAR