Transforming Gaussian Processes With Normalizing Flows

Autor:	Maroñas, Juan, Hamelijnck, Oliver, Knoblauch, Jeremias, Damoulas, Theodoros
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Gaussian Processes (GPs) can be used as flexible, non-parametric function priors. Inspired by the growing body of work on Normalizing Flows, we enlarge this class of priors through a parametric invertible transformation that can be made input-dependent. Doing so also allows us to encode interpretable prior knowledge (e.g., boundedness constraints). We derive a variational approximation to the resulting Bayesian inference problem, which is as fast as stochastic variational GP regression (Hensman et al., 2013; Dezfouli and Bonilla,2015). This makes the model a computationally efficient alternative to other hierarchical extensions of GP priors (Lazaro-Gredilla,2012; Damianou and Lawrence, 2013). The resulting algorithm's computational and inferential performance is excellent, and we demonstrate this on a range of data sets. For example, even with only 5 inducing points and an input-dependent flow, our method is consistently competitive with a standard sparse GP fitted using 100 inducing points. Comment: AISTATS 2021, camera ready
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.01596 Zobrazit plný text záznamu View this record from Arxiv