TA3b.3

Bayesian Koopman-inspired Time-series Forecasting with Graph-encoded Exogenous Information

Theodore Glavas, McGill University, Canada; Sebastian Pütz, Karlsrühe Institute of Technology, Canada; Mark Coates, McGill University, Canada; Boris Oreshkin, Amazon, Canada
Session:
TA3b: Bayesian Learning and Inference on Graphs Lecture
Track:
Networks and Graphs
Location:
Room 3
Presentation Time:
Tue, 27 Oct, 11:05 - 11:30 PT (UTC -7)
Session Co-Chairs:
Daniel Waxman, Basis Research Institute, United States and Kurt Butler, University of Edinburgh, United Kingdom and Victor Elvira, University of Edinburgh, United Kingdom and Petar M. Djurić, Stony Brook University, United States
Presentation
Discussion
Resources
No resources available.
Session TA3b
TA3b.1: Bayesian Learning of Graph-Based Interaction Potentials with Applications to Studying Animal Behavior
Daniel Waxman, Matthew Levine, Basis Research Institute, United States; Kurt Butler, University of Edinburgh, United Kingdom; Petar Djuric, Stony Brook University, United States; Emily Mackevicius, Dmitry Batenkov, Basis Research Institute, United States
TA3b.2: BAYESIAN 2D-3D POINT CLOUD REGISTRATION USING GRAPH SIMILARITY MEASURES
Younes Boutiyarzist, ISAE SUPAERO/TeSA/Collins Aerospace, France; Jean-Yves Tourneret, IRIT-ENSEEIHT/TeSA, France; François Vincent, ISAE SUPAERO, France; Philippe Salmon, Collins Aerospace, France
TA3b.3: Bayesian Koopman-inspired Time-series Forecasting with Graph-encoded Exogenous Information
Theodore Glavas, McGill University, Canada; Sebastian Pütz, Karlsrühe Institute of Technology, Canada; Mark Coates, McGill University, Canada; Boris Oreshkin, Amazon, Canada
TA3b.4: Graph Transfer Learning via Shared Latent Geometry: An EM Perspective
Tong Wu, University of Central Florida, United States; Hang Liu, University of Macau, Macao SAR of China; Anna Scaglione, Andrew Campbell, Cornell University, United States
Contacts