Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning

6 May 2021

Papers citing "Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning"

4 / 4 papers shown

Title
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean Anton Thielmann René-Marcel Kruse Thomas Kneib Benjamin Säfken 88 13 0 27 Jan 2023
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection James Enouen Yan Liu 72 20 0 19 Sep 2022
Post-hoc Concept Bottleneck Models Mert Yuksekgonul Maggie Wang James Zou 245 198 0 31 May 2022
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 251 678 0 20 Mar 2021