TA3b: Bayesian Learning and Inference on Graphs
Tue, 27 Oct, 10:15 - 11:55 PT (UTC -7)
Location: Room 3
Session Type: Lecture
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
Track: Networks and Graphs
Tue, 27 Oct, 10:15 - 10:40 PT (UTC -7)

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
Tue, 27 Oct, 10:40 - 11:05 PT (UTC -7)

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
Tue, 27 Oct, 11:05 - 11:30 PT (UTC -7)

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
Tue, 27 Oct, 11:30 - 11:55 PT (UTC -7)

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