3 papers
cs.LG2026
Learning the Structure of Triangular Transport Maps
Morten Blørstad, Pekka Parviainen, Berent Ånund Strømnes Lunde
Triangular transport maps provide a flexible approach to sampling-based probabilistic modeling, including density estimation, generative modeling, and Bayesian inference. They tran…
cs.LG2026
Evaluating Prediction Uncertainty Estimates from BatchEnsemble
Morten Blørstad, Herman Jangsett Mostein, Nello Blaser +1
Deep learning models struggle with uncertainty estimation. Many approaches are either computationally infeasible or underestimate uncertainty. We investigate \textit{BatchEnsemble}…
cs.LG2024
Stable Update of Regression Trees
Morten Blørstad, Berent Å. S. Lunde, Nello Blaser
Updating machine learning models with new information usually improves their predictive performance, yet, in many applications, it is also desirable to avoid changing the model pre…