Dynamic Online Ensembles of Basis Expansions

arXiv:2405.01365v1 Announce Type: new
Abstract: Practical Bayesian learning often requires (1) online inference, (2) dynamic models, and (3) ensembling over multiple different models. Recent advances have shown how to use random feature approximations to achieve scalable, online ensembling of Gaussian processes with desirable theoretical properties and fruitful applications. One key to these methods' success is the inclusion of a random walk on the model parameters, which makes models dynamic. We show that these methods can be generalized easily to any …

abstract advances applications arxiv bayesian cs.lg dynamic eess.sp feature gaussian processes inference key multiple practical processes random scalable stat.ml success type