Decision Forests

All research related to Decision Forests. A full list of topics is available on my research page.

1. ⭐ Minimizing and quantifying uncertainty in AI-informed decisions: Applications in medicine

   Samuel D. Curtis*, Sambit Panda*, Adam Li*, Haoyin Xu, Yuxin Bai, Itsuki Ogihara, Eliza O’Reilly, Yuxuan Wang, Lisa Dobbyn, Maria Popoli, Janine Ptak, Nadine Nehme, Natalie Silliman, Jeanne Tie, Peter Gibbs, Lan T. Ho-Pham, Bich N. H. Tran, Thach S. Tran, Tuan V. Nguyen, Ehsan Irajizad, Michael Goggins, Christopher L. Wolfgang, Tian-Li Wang, Ie-Ming Shih, Amanda Fader, Anne Marie Lennon, Ralph H. Hruban, Chetan Bettegowda, Lucy Gilbert, Kenneth W. Kinzler, Nickolas Papadopoulos, Bert Vogelstein, Joshua T. Vogelstein, Christopher Douville
   PNAS, 2025

   Introduces MIGHT, which helps quantify the amount of predictive information in very high-dimensional data. This was then used to develop and evaluate a biomedical assay to detect cancer early.

2. 📝 Simplest Streaming Trees

   Haoyin Xu, Jayanta Dey, Sambit Panda, Joshua T. Vogelstein
   arXiv, 2023

   Developed a streaming algorithm for decision trees based on the simplest possible extension of them.

3. 📝 Learning Interpretable Characteristic Kernels via Decision Forests

   Sambit Panda*, Cencheng Shen*, Joshua T. Vogelstein
   arXiv, 2023

   Demonstrates the kernel derived from random forest is characteristic and develops a hypothesis test based on that fact (KMERF).

4. 📝 When are Deep Networks really better than Decision Forests at small sample sizes, and how?

   Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, Carey E. Priebe
   arXiv, 2021

   Illustrates that forest based methods excel at tabular data classification at small sample sizes while networks excel at larger sample sizes.