2 papers
cs.LG2026
Practical Deep Heteroskedastic Regression
Mikkel Jordahn, Jonas Vestergaard Jensen, James Harrison +2
Uncertainty quantification (UQ) in deep learning regression is of wide interest, as it supports critical applications including sequential decision making and risk-sensitive tasks.…
cs.LG2025
On Local Posterior Structure in Deep Ensembles
Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt +1
Bayesian Neural Networks (BNNs) often improve model calibration and predictive uncertainty quantification compared to point estimators such as maximum-a-posteriori (MAP). Similarly…