Multivariate Bayesian Last Layer for Regression: Uncertainty Quantification and Disentanglement
<|reference_start|>Multivariate Bayesian Last Layer for Regression: Uncertainty Quantification and Disentanglement: We present new Bayesian Last Layer models in the setting of multivariate regression under heteroscedastic noise, and propose an optimization algorithm for parameter learning. Bayesian Last Layer combines Bayesian modelling of the predictive distribution with neural networks for parameterization of the prior, and has the attractive property of uncertainty quantification with a single forward pass. The proposed framework is capable of disentangling the aleatoric and epistemic uncertainty, and can be used to transfer a canonically trained deep neural network to new data domains with uncertainty-aware capability.<|reference_end|>
@article{wang2024multivariate,
    title={Multivariate Bayesian Last Layer for Regression: Uncertainty
  Quantification and Disentanglement},
    author={Han Wang, Eiji Kawasaki, Guillaume Damblin, Geoffrey Daniel},
    journal={arXiv preprint arXiv:2405.01761},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01761},
    primaryClass={stat.ML cs.LG}
}