On Validating, Repairing and Refining Heuristic ML Explanations

4 July 2019

Papers citing "On Validating, Repairing and Refining Heuristic ML Explanations"

7 / 7 papers shown

Title
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 103 938 0 20 Jun 2018
Reachability Analysis of Deep Neural Networks with Provable Guarantees Wenjie Ruan Xiaowei Huang Marta Kwiatkowska AAML 58 270 0 06 May 2018
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu M. C. Hughes S. Parbhoo Maurizio Zazzi Volker Roth Finale Doshi-Velez AI4CE 114 281 0 16 Nov 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 290 1,849 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 198 935 0 21 Oct 2016
Certifying and removing disparate impact Michael Feldman Sorelle A. Friedler John Moeller C. Scheidegger Suresh Venkatasubramanian FaML 127 1,978 0 11 Dec 2014
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements Craig Boutilier Ronen I. Brafman Carmel Domshlak H. H. Hoos David Poole 131 926 0 30 Jun 2011