Contemporary Symbolic Regression Methods for Interpretable Machine Learning

Speaker: William La Cava, PhD, at Boston Children's Hospital

Date: September 17, 2021 at 09:30AM - 10:30AM

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. 


Publications

Hopper RK, Abman SH, Elia EG, Avitabile CM, Yung D, Mullen MP, Austin ED, Bates A, Handler SS, Feinstein JA, Ivy DD, Kinsella JP, Mandl KD, Raj JU, Sleeper LA, . Pulmonary Hypertension in Children with Down Syndrome: Results from the Pediatric Pulmonary Hypertension Network Registry. The Journal of pediatrics 2022.

Wang X, Zhang HG, Xiong X, Hong C, Weber GM, Brat GA, Bonzel CL, Luo Y, Duan R, Palmer NP, Hutch MR, Gutiérrez-Sacristán A, Bellazzi R, Chiovato L, Cho K, Dagliati A, Estiri H, García-Barrio N, Griffier R, Hanauer DA, Ho YL, Holmes JH, Keller MS, Klann MEng JG, L'Yi S, Lozano-Zahonero S, Maidlow SE, Makoudjou A, Malovini A, Moal B, Moore JH, Morris M, Mowery DL, Murphy SN, Neuraz A, Yuan Ngiam K, Omenn GS, Patel LP, Pedrera-Jiménez M, Prunotto A, Jebathilagam Samayamuthu M, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano-Balazote P, South AM, Tan ALM, Tan BWL, Tibollo V, Tippmann P, Visweswaran S, Xia Z, Yuan W, Zöller D, Kohane IS, Avillach P, Guo Z, Cai T, . SurvMaximin: Robust federated approach to transporting survival risk prediction models. Journal of biomedical informatics 2022.

Torous J, Stern AD, Bourgeois FT. Regulatory considerations to keep pace with innovation in digital health products. NPJ digital medicine 2022.

Levy S, Wisk LE, Minegishi M, Ertman B, Lunstead J, Brogna M, Weitzman ER. Association of Screening and Brief Intervention With Substance Use in Massachusetts Middle and High Schools. JAMA network open 2022.

Nielsen M, Presti M, Sztupinszki Z, Jensen AWP, Draghi A, Chamberlain CA, Schina A, Yde CW, Wojcik J, Szallasi Z, Crowther MD, Svane IM, Donia M. Co-existing alterations of MHC class I antigen presentation and IFNγ signaling mediate acquired resistance of melanoma to post-PD-1 immunotherapy. Cancer immunology research 2022.