Regularizing Black-box Models for Improved Interpretability

18 February 2019

Papers citing "Regularizing Black-box Models for Improved Interpretability"

9 / 9 papers shown

Title
Joint Explainability-Performance Optimization With Surrogate Models for AI-Driven Edge Services Foivos Charalampakos Thomas Tsouparopoulos Iordanis Koutsopoulos 49 1 0 10 Mar 2025
Explanation Regularisation through the Lens of Attributions Pedro Ferreira Wilker Aziz Ivan Titov 43 1 0 23 Jul 2024
Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales Lucas Resck Marcos M. Raimundo Jorge Poco 44 1 0 03 Apr 2024
Use-Case-Grounded Simulations for Explanation Evaluation Valerie Chen Nari Johnson Nicholay Topin Gregory Plumb Ameet Talwalkar FAtt ELM 22 24 0 05 Jun 2022
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 30 11 0 13 May 2022
What to Learn, and How: Toward Effective Learning from Rationales Samuel Carton Surya Kanoria Chenhao Tan 32 22 0 30 Nov 2021
Defense Against Explanation Manipulation Ruixiang Tang Ninghao Liu Fan Yang Na Zou Xia Hu AAML 39 11 0 08 Nov 2021
Shapley Explanation Networks Rui Wang Xiaoqian Wang David I. Inouye TDI FAtt 19 44 0 06 Apr 2021
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers Hanjie Chen Yangfeng Ji AAML VLM 13 62 0 01 Oct 2020