BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Venue Radar//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:COLT 2027 Calendar BEGIN:VEVENT UID:colt-2027-deadline-full-paper@scientific-conference-calendar DTSTAMP:19700101T000000Z DTSTART:20270204T115900Z DTEND:20270204T125900Z SUMMARY:[Learning Theory & Statistical Foundations][Machine Learning] COLT 2027 - Submission deadline (estimated) DESCRIPTION:Conference title: Conference on Learning Theory\nWebsite: https ://learningtheory.org/colt2027/\nSource URL: https://learningtheory.org/c olt2027/\nLocation: Tokyo\, Japan\nTopics: Learning Theory & Statistical Foundations\, Machine Learning\nSize: M\nSubmission type: Paper-based / t heory-focused\nConfidence: estimated\nLast checked date: 2026-10-05\nNote s: Official COLT 2027 page confirms Tokyo and Jun 28-Jul 2\, 2027\; submi ssion milestones remain estimates until the current CFP publishes them.\n Scope: Theoretical foundations of learning at the intersection of compute r science\, statistics\, and applied mathematics. Characteristic work stu dies learnability\, sample and computational complexity\, online learning \, optimization\, bandits\, and generalization. Neural-network\, causal\, reinforcement-learning\, privacy\, and fairness papers belong here when the theoretical contribution is central.\nAdditional topics: Deep Represe ntation Learning\, Probabilistic\, Causal & Uncertainty ML\, Reinforcemen t Learning\, Privacy-preserving ML\, Fairness & Responsible AI\nScope sou rces (2026): https://learningtheory.org/colt2026/cfp.html\nScope last che cked: 2026-10-07 URL:https://learningtheory.org/colt2027/ END:VEVENT BEGIN:VEVENT UID:colt-2027-deadline-workshop@scientific-conference-calendar DTSTAMP:19700101T000000Z DTSTART:20270507T115900Z DTEND:20270507T125900Z SUMMARY:[Learning Theory & Statistical Foundations][Machine Learning] COLT 2027 - Workshop/tutorial/community proposal deadline (estimated) DESCRIPTION:Conference title: Conference on Learning Theory\nWebsite: https ://learningtheory.org/colt2027/\nSource URL: https://learningtheory.org/c olt2027/\nLocation: Tokyo\, Japan\nTopics: Learning Theory & Statistical Foundations\, Machine Learning\nSize: M\nSubmission type: Paper-based / t heory-focused\nConfidence: estimated\nLast checked date: 2026-10-05\nNote s: Official COLT 2027 page confirms Tokyo and Jun 28-Jul 2\, 2027\; submi ssion milestones remain estimates until the current CFP publishes them.\n Scope: Theoretical foundations of learning at the intersection of compute r science\, statistics\, and applied mathematics. Characteristic work stu dies learnability\, sample and computational complexity\, online learning \, optimization\, bandits\, and generalization. Neural-network\, causal\, reinforcement-learning\, privacy\, and fairness papers belong here when the theoretical contribution is central.\nAdditional topics: Deep Represe ntation Learning\, Probabilistic\, Causal & Uncertainty ML\, Reinforcemen t Learning\, Privacy-preserving ML\, Fairness & Responsible AI\nScope sou rces (2026): https://learningtheory.org/colt2026/cfp.html\nScope last che cked: 2026-10-07 URL:https://learningtheory.org/colt2027/ END:VEVENT BEGIN:VEVENT UID:colt-2027-conference@scientific-conference-calendar DTSTAMP:19700101T000000Z DTSTART;VALUE=DATE:20270628 DTEND;VALUE=DATE:20270703 SUMMARY:[Learning Theory & Statistical Foundations][Machine Learning] COLT 2027 - Conference (estimated) DESCRIPTION:Conference title: Conference on Learning Theory\nWebsite: https ://learningtheory.org/colt2027/\nSource URL: https://learningtheory.org/c olt2027/\nLocation: Tokyo\, Japan\nTopics: Learning Theory & Statistical Foundations\, Machine Learning\nSize: M\nSubmission type: Paper-based / t heory-focused\nConfidence: estimated\nLast checked date: 2026-10-05\nNote s: Official COLT 2027 page confirms Tokyo and Jun 28-Jul 2\, 2027\; submi ssion milestones remain estimates until the current CFP publishes them.\n Scope: Theoretical foundations of learning at the intersection of compute r science\, statistics\, and applied mathematics. Characteristic work stu dies learnability\, sample and computational complexity\, online learning \, optimization\, bandits\, and generalization. Neural-network\, causal\, reinforcement-learning\, privacy\, and fairness papers belong here when the theoretical contribution is central.\nAdditional topics: Deep Represe ntation Learning\, Probabilistic\, Causal & Uncertainty ML\, Reinforcemen t Learning\, Privacy-preserving ML\, Fairness & Responsible AI\nScope sou rces (2026): https://learningtheory.org/colt2026/cfp.html\nScope last che cked: 2026-10-07 URL:https://learningtheory.org/colt2027/ LOCATION:Tokyo\, Japan END:VEVENT END:VCALENDAR