Title: Generalization Error from Approximate Probability Propagation Method
Speaker: Ms. Ayaka Sakata (Associate Professor, The Institute of Statistical Mathematics)
Abstract: Approximate probability propagation methods are widely used to efficiently approximate inference in large-scale stochastic models. The method simplifies computation by assuming a local tree structure and evaluating the marginalized distribution based on a graphical representation of the stochastic model. The procedure can be interpreted as implicitly including leave-one-out cross-validation error evaluation. In linear models, the expression of leave-one-out cross-validation error can be obtained analytically, but using an interpretation based on the approximate probability propagation method, this expression can be extended to a more general framework. In this presentation, the approximate probability propagation method and its statistical implications will be explained, and various estimator evaluation methods for generalization errors, including leave-one-out cross-validation errors, will be presented.<\/p>\n\n\n\n
21st March, 2025\uff08Fri,\uff0918:00\uff5e20:00<\/p>\n<\/dd>\n\n\n\n
Held online<\/p>\n<\/dd>\n\n\n\n
Co-organizer (HRAM The Japan Society for Industrial and Applied Mathematics, D-DRIVE National Network)<\/p>\n<\/dd>\n\n\n\n
Free\uff08Advance registration required\uff09<\/p>\n\n\n\n