2 papers
cond-mat.mtrl-sci2025
When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
Ashley S. Dale, Kangming Li, Brian DeCost +4
Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty…
stat.ME2025
Nonparametric learning of covariate-based Markov jump processes using RKHS techniques
Yuchen Han, Arnab Ganguly, Riten Mitra
We propose a novel nonparametric approach for linking covariates to Continuous Time Markov Chains (CTMCs) using the mathematical framework of Reproducing Kernel Hilbert Spaces (RKH…