1 citations · 2 across the 3 of their papers we have counts for
3 papers
Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems
Benjamin Kolicic, Alberto Caron, Chris Hicks +1
In this paper, we address the critical need for interpretable and uncertainty-aware machine learning models in the context of online learning for high-risk industries, particularly…
Structure Learning with Adaptive Random Neighborhood Informed MCMC
Alberto Caron, Xitong Liang, Samuel Livingstone +1
In this paper, we introduce a novel MCMC sampler, PARNI-DAG, for a fully-Bayesian approach to the problem of structure learning under observational data. Under the assumption of ca…
Interpretable Deep Causal Learning for Moderation Effects
Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models. In particular, we focus on the problem of…