9 citations · 27 across the 10 of their papers we have counts for
4 papers · 1 filter
ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs
Landon Butler, Abhineet Agarwal, Justin Singh Kang +3
Large Language Models (LLMs) have achieved remarkable performance by capturing complex interactions between input features. To identify these interactions, most existing approaches…
SPEX: Scaling Feature Interaction Explanations for LLMs
Justin Singh Kang, Landon Butler, Abhineet Agarwal +4
Large language models (LLMs) have revolutionized machine learning due to their ability to capture complex interactions between input features. Popular post-hoc explanation methods…
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…