104 citations · 113 across the 4 of their papers we have counts for
4 papers
The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms
Nicholas I-Hsien Kuo, Mark N. Polizzotto, Simon Finfer +6
In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not ope…
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible
Melanie F. Pradier, Javier Zazo, Sonali Parbhoo +3
We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predict…
Regional Tree Regularization for Interpretability in Black Box Models
Mike Wu, Sonali Parbhoo, Michael Hughes +5
The lack of interpretability remains a barrier to the adoption of deep neural networks. Recently, tree regularization has been proposed to encourage deep neural networks to resembl…
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
Mike Wu, Michael C. Hughes, Sonali Parbhoo +3
The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step…