Decision Tree
Decision trees are a fundamental machine learning model aiming to create interpretable predictive models by recursively partitioning data based on feature values. Current research emphasizes improving their accuracy, efficiency, and robustness, particularly within ensemble methods like random forests and gradient boosted trees, as well as exploring novel architectures like auto-regressive decision trees and their application in areas such as natural language processing. This ongoing work addresses challenges like vulnerability to adversarial attacks, handling noisy data, and optimizing for both accuracy and interpretability, impacting diverse fields from fraud detection to cybersecurity and beyond.
Papers
Amortized SHAP values via sparse Fourier function approximation
Ali Gorji, Andisheh Amrollahi, Andreas Krause
Understanding Gradient Boosting Classifier: Training, Prediction, and the Role of $γ_j$
Hung-Hsuan Chen
SMART: A Flexible Approach to Regression using Spline-Based Multivariate Adaptive Regression Trees
William Pattie, Arvind Krishna
On the Power of Decision Trees in Auto-Regressive Language Modeling
Yulu Gan, Tomer Galanti, Tomaso Poggio, Eran Malach
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models
Ricardo Knauer, Mario Koddenbrock, Raphael Wallsberger, Nicholas M. Brisson, Georg N. Duda, Deborah Falla, David W. Evans, Erik Rodner