BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Venue Radar//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:ProbML 2027 Calendar
BEGIN:VEVENT
UID:probml-2027-deadline-extended-abstract@scientific-conference-calendar
DTSTAMP:19700101T000000Z
DTSTART:20270321T115900Z
DTEND:20270321T125900Z
SUMMARY:[Probabilistic\, Causal & Uncertainty ML][Learning Theory & Statist
 ical Foundations] ProbML 2027 - Non-archival workshop submission deadline
  (estimated)
DESCRIPTION:Conference title: Symposium on Probabilistic Machine Learning\n
 Website: https://probml.cc/\nSource URL: https://probml.cc/call/\nLocatio
 n: To be announced\nTopics: Probabilistic\, Causal & Uncertainty ML\, Lea
 rning Theory & Statistical Foundations\, Machine Learning\, Healthcare & 
 Clinical AI\nSize: S\nSubmission type: Paper-based / non-archival extende
 d abstracts\nConfidence: estimated\nLast checked date: 2026-10-07\nNotes:
  Formerly AABI. Recent editions recur annually\, but no official 2027 sch
 edule was found. Meeting and submission dates project ProbML 2026 timing\
 ; location\, ICML co-location\, and the 2026 healthcare/climate applicati
 on focus require reconfirmation. The prior fast track requires a paper ac
 cepted elsewhere\, so it is not treated as a fresh submission route. Size
  S is an editorial estimate for this specialist symposium.\nScope: Probab
 ilistic and Bayesian machine learning\, emphasizing approximate inference
 \, variational and Monte Carlo methods\, Gaussian processes\, graphical a
 nd state-space models\, calibration\, and decisions under uncertainty. In
 cludes Bayesian optimization and uncertainty-aware deep learning. The rev
 iewed 2026 application focus is healthcare and climate\; probabilistic re
 asoning remains essential to a suitable contribution.\nAdditional topics:
  Deep Representation Learning\, Reinforcement Learning\, Time Series & Se
 quential Data\, Robustness & Safety\nScope sources (2026): https://probml
 .cc/call/\nScope last checked: 2026-10-07
URL:https://probml.cc/call/
END:VEVENT
BEGIN:VEVENT
UID:probml-2027-deadline-full-paper@scientific-conference-calendar
DTSTAMP:19700101T000000Z
DTSTART:20270321T115900Z
DTEND:20270321T125900Z
SUMMARY:[Probabilistic\, Causal & Uncertainty ML][Learning Theory & Statist
 ical Foundations] ProbML 2027 - Proceedings paper submission deadline (es
 timated)
DESCRIPTION:Conference title: Symposium on Probabilistic Machine Learning\n
 Website: https://probml.cc/\nSource URL: https://probml.cc/call/\nLocatio
 n: To be announced\nTopics: Probabilistic\, Causal & Uncertainty ML\, Lea
 rning Theory & Statistical Foundations\, Machine Learning\, Healthcare & 
 Clinical AI\nSize: S\nSubmission type: Paper-based / non-archival extende
 d abstracts\nConfidence: estimated\nLast checked date: 2026-10-07\nNotes:
  Formerly AABI. Recent editions recur annually\, but no official 2027 sch
 edule was found. Meeting and submission dates project ProbML 2026 timing\
 ; location\, ICML co-location\, and the 2026 healthcare/climate applicati
 on focus require reconfirmation. The prior fast track requires a paper ac
 cepted elsewhere\, so it is not treated as a fresh submission route. Size
  S is an editorial estimate for this specialist symposium.\nScope: Probab
 ilistic and Bayesian machine learning\, emphasizing approximate inference
 \, variational and Monte Carlo methods\, Gaussian processes\, graphical a
 nd state-space models\, calibration\, and decisions under uncertainty. In
 cludes Bayesian optimization and uncertainty-aware deep learning. The rev
 iewed 2026 application focus is healthcare and climate\; probabilistic re
 asoning remains essential to a suitable contribution.\nAdditional topics:
  Deep Representation Learning\, Reinforcement Learning\, Time Series & Se
 quential Data\, Robustness & Safety\nScope sources (2026): https://probml
 .cc/call/\nScope last checked: 2026-10-07
URL:https://probml.cc/call/
END:VEVENT
BEGIN:VEVENT
UID:probml-2027-conference@scientific-conference-calendar
DTSTAMP:19700101T000000Z
DTSTART;VALUE=DATE:20270705
DTEND;VALUE=DATE:20270706
SUMMARY:[Probabilistic\, Causal & Uncertainty ML][Learning Theory & Statist
 ical Foundations] ProbML 2027 - Conference (estimated)
DESCRIPTION:Conference title: Symposium on Probabilistic Machine Learning\n
 Website: https://probml.cc/\nSource URL: https://probml.cc/\nLocation: To
  be announced\nTopics: Probabilistic\, Causal & Uncertainty ML\, Learning
  Theory & Statistical Foundations\, Machine Learning\, Healthcare & Clini
 cal AI\nSize: S\nSubmission type: Paper-based / non-archival extended abs
 tracts\nConfidence: estimated\nLast checked date: 2026-10-07\nNotes: Form
 erly AABI. Recent editions recur annually\, but no official 2027 schedule
  was found. Meeting and submission dates project ProbML 2026 timing\; loc
 ation\, ICML co-location\, and the 2026 healthcare/climate application fo
 cus require reconfirmation. The prior fast track requires a paper accepte
 d elsewhere\, so it is not treated as a fresh submission route. Size S is
  an editorial estimate for this specialist symposium.\nScope: Probabilist
 ic and Bayesian machine learning\, emphasizing approximate inference\, va
 riational and Monte Carlo methods\, Gaussian processes\, graphical and st
 ate-space models\, calibration\, and decisions under uncertainty. Include
 s Bayesian optimization and uncertainty-aware deep learning. The reviewed
  2026 application focus is healthcare and climate\; probabilistic reasoni
 ng remains essential to a suitable contribution.\nAdditional topics: Deep
  Representation Learning\, Reinforcement Learning\, Time Series & Sequent
 ial Data\, Robustness & Safety\nScope sources (2026): https://probml.cc/c
 all/\nScope last checked: 2026-10-07
URL:https://probml.cc/
LOCATION:To be announced
END:VEVENT
END:VCALENDAR