Contemporary Symbolic Regression Methods for Interpretable Machine Learning
Speaker: William La Cava, PhD, Faculty, Computational Health Informatics Program at Boston Children's Hospital
Most interpretable machine learning research focuses on explaining the outputs of black-box models. A different, and promising, approach is to use machine learning to find the simplest possible model that meets certain performance criteria; this is the pursuit of symbolic regression. In this talk I will discuss the concepts of interpretability and explainability, and how they are used in the machine learning world. I will then discuss a pre-print that will be published in the Neurips Datasets and Benchmarks track later this year. In it, we attempt to benchmark many different approaches to symbolic regression on hundreds of problems in order to determine the strengths and weaknesses of current methods. I will discuss what lies ahead and implications for how clinicians and patients receive and process models that increasingly appear in the health system.
This event is only open to Boston Children's staff. If you would like to attend the Zoom details, please email CHIP@childrens.harvard.edu.
William La Cava is a new member of the faculty in CHIP. He received his PhD from UMass Amherst and did his postdoctoral work at University of Pennsylvania as part of the Institute for Biomedical Informatics. His work concerns the interpretability and fairness of predictive health models.
