3 papers
cs.LG2026
path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
Claudio Meggio, Johan Pensar, Riccardo De Bin
We present path_boost, a Python package for interpretable supervised learning on graph-structured input data. The package implements PathBoost, a gradient boosting algorithm that a…
physics.chem-ph2026
tmQM-RDF Dataset: a Knowledge Graph Representing Transition Metal Complexes
Luca Cibinel, Trond Linjordet, Johan Pensar +3
Transition Metal Complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs a…
stat.ME2025
Bayesian estimation of causal effects from observational categorical data
Vera Kvisgaard, Johan Pensar
We present a Bayesian procedure for estimation of pairwise intervention effects in a high-dimensional system of categorical variables. We assume that we have observational data gen…