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TZID:Europe/Paris
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DTSTART:20260329T030000
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SUMMARY:TabICL vs scikit-learn for predictive uncertainty quantification -
  Olivier Grisel
DTSTART;TZID=Europe/Paris:20261125T105000
DTEND;TZID=Europe/Paris:20261125T112000
DTSTAMP:20260930T122049Z
UID:pretalx-compute-paris-2026-MKRCE8@pretalx.com
DESCRIPTION:TabICL is a fully-open source\, state of the art Tabular Found
 ational Model (TFM). TFMs are pretrained on large amount of synthetic data
  and have been advertised as being able to provide well calibrated probabi
 listic predictions both for classification and regression tasks.\n\nIn thi
 s presentation\, we will present the results of a comparative study of the
  predictive capabilities of TabICL versus several scikit-learn based alter
 natives. In particular\, we will contrast the following aspects: simplicit
 y of development\, computational and statistical performance in small and 
 large sample size regimes. Statistical performance will be evaluated with 
 strictly proper scoring rules that assess both the calibration and sharpne
 ss of predictions on left out data.
LOCATION:Auditorium
URL:https://pretalx.com/compute-paris-2026/talk/MKRCE8/
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