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Gradient Boosted Normalizing Flows
Robert Giaquinto · Arindam Banerjee

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1177

By chaining a sequence of differentiable invertible transformations, normalizing flows (NF) provide an expressive method of posterior approximation, exact density evaluation, and sampling. The trend in normalizing flow literature has been to devise deeper, more complex transformations to achieve greater flexibility. We propose an alternative: Gradient Boosted Normalizing Flows (GBNF) model a density by successively adding new NF components with gradient boosting. Under the boosting framework, each new NF component optimizes a weighted likelihood objective, resulting in new components that are fit to the suitable residuals of the previously trained components. The GBNF formulation results in a mixture model structure, whose flexibility increases as more components are added. Moreover, GBNFs offer a wider, as opposed to strictly deeper, approach that improves existing NFs at the cost of additional training---not more complex transformations. We demonstrate the effectiveness of this technique for density estimation and, by coupling GBNF with a variational autoencoder, generative modeling of images. Our results show that GBNFs outperform their non-boosted analog, and, in some cases, produce better results with smaller, simpler flows.

Author Information

Robert Giaquinto (University of Minnesota, Twin Cities)

My research interests are in machine learning. In particular, I work on deep generative and probabilistic models, normalizing flows, approximate inference, with applications in natural language processing, image generation, information retrieval, and spatiotemporal prediction. Github: http://github.com/robert-giaquinto/ Website: https://www-users.cs.umn.edu/~smit7982/ Linkedin: www.linkedin.com/in/robert-giaquinto-phd

Arindam Banerjee (University of Minnesota, Twin Cities)

Arindam Banerjee is a Professor at the Department of Computer & Engineering and a Resident Fellow at the Institute on the Environment at the University of Minnesota, Twin Cities. His research interests are in machine learning, data mining, and applications in complex real-world problems in different areas including climate science, ecology, recommendation systems, text analysis, and finance. He has won several awards, including the NSF CAREER award (2010), the IBM Faculty Award (2013), and six best paper awards in top-tier conferences.

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