BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//juliacon2023//talk//JQWNNP
BEGIN:VTIMEZONE
TZID:EST
BEGIN:STANDARD
DTSTART:20001029T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10;UNTIL=20061029T070000Z
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
END:STANDARD
BEGIN:STANDARD
DTSTART:20071104T030000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000402T030000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4;UNTIL=20060402T080000Z
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20070311T030000
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-juliacon2023-JQWNNP@pretalx.com
DTSTART;TZID=EST:20230728T103000
DTEND;TZID=EST:20230728T110000
DESCRIPTION:We propose [`ConformalPrediction.jl`](https://github.com/pat-al
 t/ConformalPrediction.jl): a Julia package for Predictive Uncertainty Quan
 tification in Machine Learning (ML) through Conformal Prediction. It works
  with supervised models trained in [`MLJ.jl`](https://alan-turing-institut
 e.github.io/MLJ.jl/dev/)\, a popular comprehensive ML framework for Julia.
  Conformal Prediction is easy-to-understand\, easy-to-use and model-agnost
 ic and it works under minimal distributional assumptions.
DTSTAMP:20260515T113503Z
LOCATION:26-100
SUMMARY:Predictive Uncertainty Quantification in Machine Learning - Patrick
  Altmeyer
URL:https://pretalx.com/juliacon2023/talk/JQWNNP/
END:VEVENT
END:VCALENDAR