BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Venue Radar//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:LoG 2026 Calendar BEGIN:VEVENT UID:log-2026-deadline-abstract@scientific-conference-calendar DTSTAMP:19700101T000000Z DTSTART:20260806T115900Z DTEND:20260806T125900Z SUMMARY:[Machine Learning][Deep Representation Learning] LoG 2026 - Abstrac t registration deadline (both tracks) DESCRIPTION:Conference title: Learning on Graphs Conference\nWebsite: https ://logconference.org/\nSource URL: https://logconference.org/cfp/\nLocati on: Boston\, Massachusetts\, USA\nTopics: Machine Learning\, Deep Represe ntation Learning\, Graphs & Graph Learning\, Learning Theory & Statistica l Foundations\nSize: M\nSubmission type: Paper-based / non-archival exten ded abstracts\nConfidence: confirmed\nLast checked date: 2026-10-07\nNote s: Fifth annual edition\, in person at Northeastern University. Extended Aug 5/7 deadlines replace the struck-through July 29/Aug 1 dates. Proceed ings papers and non-archival extended abstracts share the registration an d final submission cutoffs\, represented as one route. The TMLR presentat ion track requires an already published paper\, so it is not a new-paper opportunity. LoG is distinct from LOD/AIS. Size M is an editorial estimat e.\nScope: Learning on graphs and geometry: graph neural networks\, equiv ariant models\, graph kernels\, geometric and graph generative models\, a nd statistical theory for structured data. Graph signal processing\, caus al graph models\, robustness\, and scalable graph-learning infrastructure are meaningful extensions. A graph or geometric contribution should rema in central\, even in an application such as language or molecular modelin g.\nAdditional topics: Complex Systems & Network Science\, Probabilistic\ , Causal & Uncertainty ML\, Signal Processing\, Robustness & Safety\, ML Systems & Infrastructure\nScope sources (2026): https://logconference.org /cfp/\nScope last checked: 2026-10-07 URL:https://logconference.org/cfp/ END:VEVENT BEGIN:VEVENT UID:log-2026-deadline-full-paper@scientific-conference-calendar DTSTAMP:19700101T000000Z DTSTART:20260808T115900Z DTEND:20260808T125900Z SUMMARY:[Machine Learning][Deep Representation Learning] LoG 2026 - Paper / extended abstract submission deadline DESCRIPTION:Conference title: Learning on Graphs Conference\nWebsite: https ://logconference.org/\nSource URL: https://logconference.org/cfp/\nLocati on: Boston\, Massachusetts\, USA\nTopics: Machine Learning\, Deep Represe ntation Learning\, Graphs & Graph Learning\, Learning Theory & Statistica l Foundations\nSize: M\nSubmission type: Paper-based / non-archival exten ded abstracts\nConfidence: confirmed\nLast checked date: 2026-10-07\nNote s: Fifth annual edition\, in person at Northeastern University. Extended Aug 5/7 deadlines replace the struck-through July 29/Aug 1 dates. Proceed ings papers and non-archival extended abstracts share the registration an d final submission cutoffs\, represented as one route. The TMLR presentat ion track requires an already published paper\, so it is not a new-paper opportunity. LoG is distinct from LOD/AIS. Size M is an editorial estimat e.\nScope: Learning on graphs and geometry: graph neural networks\, equiv ariant models\, graph kernels\, geometric and graph generative models\, a nd statistical theory for structured data. Graph signal processing\, caus al graph models\, robustness\, and scalable graph-learning infrastructure are meaningful extensions. A graph or geometric contribution should rema in central\, even in an application such as language or molecular modelin g.\nAdditional topics: Complex Systems & Network Science\, Probabilistic\ , Causal & Uncertainty ML\, Signal Processing\, Robustness & Safety\, ML Systems & Infrastructure\nScope sources (2026): https://logconference.org /cfp/\nScope last checked: 2026-10-07 URL:https://logconference.org/cfp/ END:VEVENT BEGIN:VEVENT UID:log-2026-deadline-camera-ready@scientific-conference-calendar DTSTAMP:19700101T000000Z DTSTART:20261016T115900Z DTEND:20261016T125900Z SUMMARY:[Machine Learning][Deep Representation Learning] LoG 2026 - Camera- ready deadline DESCRIPTION:Conference title: Learning on Graphs Conference\nWebsite: https ://logconference.org/\nSource URL: https://logconference.org/cfp/\nLocati on: Boston\, Massachusetts\, USA\nTopics: Machine Learning\, Deep Represe ntation Learning\, Graphs & Graph Learning\, Learning Theory & Statistica l Foundations\nSize: M\nSubmission type: Paper-based / non-archival exten ded abstracts\nConfidence: confirmed\nLast checked date: 2026-10-07\nNote s: Fifth annual edition\, in person at Northeastern University. Extended Aug 5/7 deadlines replace the struck-through July 29/Aug 1 dates. Proceed ings papers and non-archival extended abstracts share the registration an d final submission cutoffs\, represented as one route. The TMLR presentat ion track requires an already published paper\, so it is not a new-paper opportunity. LoG is distinct from LOD/AIS. Size M is an editorial estimat e.\nScope: Learning on graphs and geometry: graph neural networks\, equiv ariant models\, graph kernels\, geometric and graph generative models\, a nd statistical theory for structured data. Graph signal processing\, caus al graph models\, robustness\, and scalable graph-learning infrastructure are meaningful extensions. A graph or geometric contribution should rema in central\, even in an application such as language or molecular modelin g.\nAdditional topics: Complex Systems & Network Science\, Probabilistic\ , Causal & Uncertainty ML\, Signal Processing\, Robustness & Safety\, ML Systems & Infrastructure\nScope sources (2026): https://logconference.org /cfp/\nScope last checked: 2026-10-07 URL:https://logconference.org/cfp/ END:VEVENT BEGIN:VEVENT UID:log-2026-conference@scientific-conference-calendar DTSTAMP:19700101T000000Z DTSTART;VALUE=DATE:20261120 DTEND;VALUE=DATE:20261123 SUMMARY:[Machine Learning][Deep Representation Learning] LoG 2026 - Confere nce DESCRIPTION:Conference title: Learning on Graphs Conference\nWebsite: https ://logconference.org/\nSource URL: https://logconference.org/\nLocation: Boston\, Massachusetts\, USA\nTopics: Machine Learning\, Deep Representat ion Learning\, Graphs & Graph Learning\, Learning Theory & Statistical Fo undations\nSize: M\nSubmission type: Paper-based / non-archival extended abstracts\nConfidence: confirmed\nLast checked date: 2026-10-07\nNotes: F ifth annual edition\, in person at Northeastern University. Extended Aug 5/7 deadlines replace the struck-through July 29/Aug 1 dates. Proceedings papers and non-archival extended abstracts share the registration and fi nal submission cutoffs\, represented as one route. The TMLR presentation track requires an already published paper\, so it is not a new-paper oppo rtunity. LoG is distinct from LOD/AIS. Size M is an editorial estimate.\n Scope: Learning on graphs and geometry: graph neural networks\, equivaria nt models\, graph kernels\, geometric and graph generative models\, and s tatistical theory for structured data. Graph signal processing\, causal g raph models\, robustness\, and scalable graph-learning infrastructure are meaningful extensions. A graph or geometric contribution should remain c entral\, even in an application such as language or molecular modeling.\n Additional topics: Complex Systems & Network Science\, Probabilistic\, Ca usal & Uncertainty ML\, Signal Processing\, Robustness & Safety\, ML Syst ems & Infrastructure\nScope sources (2026): https://logconference.org/cfp /\nScope last checked: 2026-10-07 URL:https://logconference.org/ LOCATION:Boston\, Massachusetts\, USA END:VEVENT END:VCALENDAR