Title: Heteroscedastic Variational Bayesian Last Layers: Modeling Input-Dependent Noise in Sparse-Data Regression
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Bayesian Neural Networks, Variational Bayesian Last Layer, Heteroscedastic Noise, Sparse Data Regression, Uncertainty Quantification

Abstract: Bayesian Neural Networks (BNNs) have been extensively studied for uncertainty quantification. To train BNNs efficiently, Variational Bayesian Last Layer (VBLL) provides a sampling-free, deterministic method, significantly reducing computational cost. However, these existing methods assume homoscedastic noise and sufficient data, while real-world industrial applications frequently encounter heteroscedastic noise, where the uncertainty level (i.e., noise) varies with input, and collecting training data in such cases is often expensive. Modeling heteroscedastic noise with sparse data is challenging, but it plays a critical role in setting appropriate safety margins for industrial applications. In this work, we propose Heteroscedastic VBLL (HVBLL) to effectively capture the input-dependent noise. We showcase the impact of noise prior on sparse-data regression, and further design a clustering-based noise level estimation method to provide reliable priors. Experimental results demonstrate that our proposed methods significantly improve the performance of BNNs under heteroscedastic and sparse-data conditions.

TL;DR: We propose Heteroscedastic VBLL to model input-dependent noise in sparse-data regression, improving uncertainty quantification for real-world industrial applications.