Relaxing the definition of equivalent mathematical expressions to get more simpler and interpretable models
The Cava laboratory is a research group in the Computational Health Informatics Program at Boston Children’s Hospital and Harvard Medical School.
We are interested in improving the interpretability and fairness of predictive models deployed in healthcare settings. We create and study algorithms that can embed these notions when working with health data.
Join the Team! If you’re looking to do research in the areas of machine learning, interpretability, fairness, and health informatics, see the opportunities here.
Recent Papers
SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation
IEEE Transactions on Evolutionary Computation
Minimum variance threshold for epsilon-lexicase selection
Proceedings of the Genetic and Evolutionary Computation Conference
Recent Posts
About our recent HUMIES award-winning algorithm for clinical prediction models
A new perspective on how this social theory relates to fair machine learning.