59 citations · 135 across the 13 of their papers we have counts for
4 papers · 2 filters
Explaining Patterns in Data with Language Models via Interpretable Autoprompting
Chandan Singh, John X. Morris, Jyoti Aneja +2
Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. In this work, we explore whether we can leverage this learne…
Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data
Keyan Nasseri, Chandan Singh, James Duncan +2
Machine learning in high-stakes domains, such as healthcare, faces two critical challenges: (1) generalizing to diverse data distributions given limited training data while (2) mai…
Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods
Abhineet Agarwal, Yan Shuo Tan, Omer Ronen +2
Tree-based models such as decision trees and random forests (RF) are a cornerstone of modern machine-learning practice. To mitigate overfitting, trees are typically regularized by…
Fast Interpretable Greedy-Tree Sums
Yan Shuo Tan, Chandan Singh, Keyan Nasseri +6
Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In…