Connecting Interpretability and Robustness in Decision Trees through Separation

14 February 2021

Papers citing "Connecting Interpretability and Robustness in Decision Trees through Separation"

10 / 10 papers shown

Title
Robustness questions the interpretability of graph neural networks: what to do? Kirill Lukyanov Georgii Sazonov Serafim Boyarsky Ilya Makarov AAML 155 0 0 05 May 2025
Interpretable Differencing of Machine Learning Models Swagatam Haldar Diptikalyan Saha Dennis L. Wei Rahul Nair Elizabeth M. Daly 16 1 0 10 Jun 2023
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes Yishay Mansour Michal Moshkovitz Cynthia Rudin FAtt 31 3 0 09 Jun 2022
Dynamic Model Tree for Interpretable Data Stream Learning Johannes Haug Klaus Broelemann Gjergji Kasneci 22 7 0 30 Mar 2022
Finding Safe Zones of policies Markov Decision Processes Lee Cohen Yishay Mansour Michal Moshkovitz 19 1 0 23 Feb 2022
Verification-Aided Deep Ensemble Selection Guy Amir Tom Zelazny Guy Katz Michael Schapira AAML 30 18 0 08 Feb 2022
Provably efficient, succinct, and precise explanations Guy Blanc Jane Lange Li-Yang Tan FAtt 34 35 0 01 Nov 2021
Interpretable Decision Trees Through MaxSAT Josep Alós Carlos Ansótegui Eduard Torres FAtt 19 8 0 26 Oct 2021
An Analysis of LIME for Text Data Dina Mardaoui Damien Garreau FAtt 134 45 0 23 Oct 2020
A Closer Look at Accuracy vs. Robustness Yao-Yuan Yang Cyrus Rashtchian Hongyang R. Zhang Ruslan Salakhutdinov Kamalika Chaudhuri OOD 68 26 0 05 Mar 2020