Tree-based Methods

Complete Paper List in Reverse Chronological Order

Selected Recent Papers on Tree-based Methods

  1. Y. S. Tan, O. Ronen, T. Saarinen, B. Yu (2024). The Computational Curse of Big Data for Bayesian Additive Regression Trees: a Hitting Time Analysis.
  2. Y. S. Tan, C. Singh, K. Nasseri, A. Agarwal, J. Duncan, O. Ronen, M. Epland, A. Kornblith, B. Yu (2022). Fast interpretable greedy-tree sums (FIGS). (imodels 🔎: a python package for fitting interpretable models contains code for FIGS).
  3. M. Behr, Y. Wang, X. Li, B. Yu (2022). Provable Boolean Interaction Recovery from Tree Ensemble obtained via Random Forests. PNAS, (theory for a tractable version of iRF, PCS-related)
  4. A. Agarwal, Y. S. Tan, O. Ronen, C. Singh, B. Yu (2022). Hierarchical shrinkage: improving accuracy and interpretability of tree-based methods. Proc. ICML (imodels 🔎: a python package for fitting interpretable models contains code for hierarchical shrinkage (HS))
  5. Y. Tan, A. Agarwal, and B. Yu (2021). A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds. Proc. AISTATS.
  6. M. Behr, K. Kumbier, A. Cordova-Palomera, M. Aguirre, E. Ashley, A. Butte, R. Arnaout, J. B. Brown, J. Preist, B. Yu (2020). Learning epistatic polygenic phenotypes with Boolean interactions. (code) (PCS inference case study)
  7. K. Kumbier, S. Sumanta, J. B. Brown, S. Celniker, and B. Yu* (2018) Refining interaction search through signed iterative Random Forests. (an enhanced version of iRF, PCS related)
  8. S. Basu, K. Kumbier, J. B. Brown, and B. Yu (2018) iterative Random Forests to discover predictive and stable high-order interactions PNAS, 115 (8), 1943-1948. (code) (PCS related)