Bayesian Linear Regression on Deep Representations

John Moberg; Lennart Svensson; Juliano Pinto; Henk Wymeersch

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arXiv · 1912.06760

Bayesian Linear Regression on Deep Representations

John Moberg·Lennart Svensson·Juliano Pinto·Henk Wymeersch

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Abstract

A simple approach to obtaining uncertainty-aware neural networks for regression is to do Bayesian linear regression (BLR) on the representation from the last hidden layer. Recent work [Riquelme et al., 2018, Azizzadenesheli et al., 2018] indicates that the method is promising, though it has been limited to homoscedastic noise. In this paper, we propose a novel variation that enables the method to flexibly model heteroscedastic noise. The method is benchmarked against two prominent alternative methods on a set of standard datasets, and finally evaluated as an uncertainty-aware model in model-based reinforcement learning. Our experiments indicate that the method is competitive with standard ensembling, and ensembles of BLR outperforms the methods we compared to.

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John Moberg, Lennart Svensson, Juliano Pinto, Henk Wymeersch. 2019-12-14. Bayesian Linear Regression on Deep Representations. https://arxiv.org/abs/1912.06760

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