Local vs. Global Interpretability: A Computational Complexity Perspective

5 June 2024

Papers citing "Local vs. Global Interpretability: A Computational Complexity Perspective"

11 / 11 papers shown

Title
Evaluating Explanations: An Explanatory Virtues Framework for Mechanistic Interpretability -- The Strange Science Part I.ii Kola Ayonrinde Louis Jaburi XAI 83 1 0 02 May 2025
On the Complexity of Global Necessary Reasons to Explain Classification M. Calautti Enrico Malizia Cristian Molinaro FAtt 63 0 0 12 Jan 2025
Logic-Based Explainability in Machine Learning Sasha Rubin LRM XAI 47 39 0 24 Oct 2022
Computing Abductive Explanations for Boosted Trees Gilles Audemard Jean-Marie Lagniez Pierre Marquis N. Szczepanski 26 12 0 16 Sep 2022
Verifying Learning-Based Robotic Navigation Systems Guy Amir Davide Corsi Raz Yerushalmi Luca Marzari D. Harel Alessandro Farinelli Guy Katz 94 37 0 26 May 2022
On Tackling Explanation Redundancy in Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 48 58 0 20 May 2022
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 144 661 0 28 Dec 2020
Model Interpretability through the Lens of Computational Complexity Pablo Barceló Mikaël Monet Jorge A. Pérez Bernardo Subercaseaux 121 94 0 23 Oct 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,212 0 23 Aug 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 254 3,684 0 28 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 231 1,837 0 03 Feb 2017