On the Trustworthiness of Tree Ensemble Explainability Methods

30 September 2021

Papers citing "On the Trustworthiness of Tree Ensemble Explainability Methods"

11 / 11 papers shown

Title
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 77 821 0 06 Nov 2019
Limitations and Biases in Facial Landmark Detection -- An Empirical Study on Older Adults with Dementia Azin Asgarian Shun Zhao A. Ashraf M. E. Browne K. Prkachin Alex Mihailidis T. Hadjistavropoulos Babak Taati CVBM 51 23 0 17 May 2019
Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models Jiayun Dong Cynthia Rudin FAtt 47 26 0 10 Jan 2019
A Hybrid Instance-based Transfer Learning Method Azin Asgarian Parinaz Sobhani Ji Chao Zhang Madalin Mihailescu Ariel Sibilia A. Ashraf Babak Taati 37 19 0 03 Dec 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 84 528 0 21 Jun 2018
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,018 0 22 May 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 405 3,813 0 28 Feb 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,706 0 10 Jun 2016
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin 814 39,062 0 09 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,033 0 16 Feb 2016
A Random Forest Guided Tour Gérard Biau Erwan Scornet AI4TS 235 2,819 0 18 Nov 2015