Hayden McTavish

Research

I work on optimization problems to facilitate interpretable machine learning. My algorithms find accurate machine learning models that succinctly and transparently communicate their entire reasoning process. Recently I have been focused on optimal decision trees, generalized additive models, and prototypical parts models.

My CV can be found here.

Publications

Interpretable Generalized Additive Models for Datasets with Missing Values, Neurips, 2024. Hayden McTavish*, Jon Donnelly*, Margo Seltzer, Cynthia Rudin

Fast Sparse Decision Tree Optimization via Reference Ensembles, AAAI, 2022. (video teaser) (talk) (code) Hayden McTavish*, Chudi Zhong*, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo Seltzer

Near-Optimal Decision Trees in a SPLIT Second, in submission. Varun Babbar*, Hayden McTavish*, Margo Seltzer, Cynthia Rudin.

Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time, CVPR, 2025. Jon Donnelly, Zhicheng Guo, Alina Jade Barnett, Hayden McTavish, Chaofan Chen, Cynthia Rudin

Selective Preference Aggregation, Neurips 2024 Workshops in Pluralistic Alignment, Behavioral ML. Shreyas Kadekodi*, Hayden McTavish*, Berk Ustun.

Engagement and Anonymity in Online Computer Science Course Forums, ICER, 2023. (slides) Mrinal Sharma*, Hayden McTavish*, Zimo Peng, Anshul Shah, Vardhan Agarwal, Caroline Sih, Emma Hogan, Ismael Villegas Molina, Adalbert Gerald Soosai Raj, Kristen Vaccaro

(*co-first authors)