Abduction-Based Explanations for Machine Learning Models

26 November 2018

Papers citing "Abduction-Based Explanations for Machine Learning Models"

45 / 45 papers shown

Title
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery Oliver Schacht Biwei Huang 12 0 0 16 May 2025
Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines R. Manuvinakurike Emanuel Moss E. A. Watkins Saurav Sahay G. Raffa L. Nachman LRM 31 0 0 01 May 2025
Axiomatic Characterisations of Sample-based Explainers Leila Amgoud Martin Cooper Salim Debbaoui FAtt 38 1 0 09 Aug 2024
Locally-Minimal Probabilistic Explanations Yacine Izza Kuldeep S. Meel Sasha Rubin 18 3 0 19 Dec 2023
Anytime Approximate Formal Feature Attribution Jinqiang Yu Graham Farr Alexey Ignatiev Peter J. Stuckey 30 2 0 12 Dec 2023
Multiple Different Black Box Explanations for Image Classifiers Hana Chockler D. A. Kelly Daniel Kroening FAtt 19 0 0 25 Sep 2023
When to Trust AI: Advances and Challenges for Certification of Neural Networks M. Kwiatkowska Xiyue Zhang AAML 28 8 0 20 Sep 2023
BELLA: Black box model Explanations by Local Linear Approximations N. Radulovic Albert Bifet Fabian M. Suchanek FAtt 37 1 0 18 May 2023
Logic for Explainable AI Adnan Darwiche 32 8 0 09 May 2023
A New Class of Explanations for Classifiers with Non-Binary Features Chunxi Ji Adnan Darwiche FAtt 26 3 0 28 Apr 2023
Finding Minimum-Cost Explanations for Predictions made by Tree Ensembles John Törnblom Emil Karlsson Simin Nadjm-Tehrani FAtt 51 0 0 16 Mar 2023
COMET: Neural Cost Model Explanation Framework Isha Chaudhary Alex Renda Charith Mendis Gagandeep Singh 21 2 0 14 Feb 2023
On the Complexity of Enumerating Prime Implicants from Decision-DNNF Circuits Alexis de Colnet Pierre Marquis 18 9 0 30 Jan 2023
SpArX: Sparse Argumentative Explanations for Neural Networks [Technical Report] Hamed Ayoobi Nico Potyka Francesca Toni 24 17 0 23 Jan 2023
Robust Explanation Constraints for Neural Networks Matthew Wicker Juyeon Heo Luca Costabello Adrian Weller FAtt 29 18 0 16 Dec 2022
VeriX: Towards Verified Explainability of Deep Neural Networks Min Wu Haoze Wu Clark W. Barrett AAML 42 10 0 02 Dec 2022
Feature Necessity & Relevancy in ML Classifier Explanations Xuanxiang Huang Martin C. Cooper António Morgado Jordi Planes Sasha Rubin FAtt 32 18 0 27 Oct 2022
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
On Computing Relevant Features for Explaining NBCs Yacine Izza Sasha Rubin 33 5 0 11 Jul 2022
Eliminating The Impossible, Whatever Remains Must Be True Jinqiang Yu Alexey Ignatiev Peter J. Stuckey Nina Narodytska Sasha Rubin 19 23 0 20 Jun 2022
Cardinality-Minimal Explanations for Monotonic Neural Networks Ouns El Harzli Bernardo Cuenca Grau Ian Horrocks FAtt 38 5 0 19 May 2022
On the Computation of Necessary and Sufficient Explanations Adnan Darwiche Chunxi Ji FAtt 13 19 0 20 Mar 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 26 41 0 15 Feb 2022
Provably efficient, succinct, and precise explanations Guy Blanc Jane Lange Li-Yang Tan FAtt 34 35 0 01 Nov 2021
Foundations of Symbolic Languages for Model Interpretability Marcelo Arenas Daniel Baez Pablo Barceló Jorge A. Pérez Bernardo Subercaseaux ReLM LRM 21 24 0 05 Oct 2021
On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI Adnan Darwiche Pierre Marquis 6 26 0 23 Aug 2021
On Efficiently Explaining Graph-Based Classifiers Xuanxiang Huang Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 34 37 0 02 Jun 2021
Explanations for Monotonic Classifiers Sasha Rubin Thomas Gerspacher M. Cooper Alexey Ignatiev Nina Narodytska FAtt 8 43 0 01 Jun 2021
A unified logical framework for explanations in classifier systems Xinghan Liu E. Lorini 15 12 0 30 May 2021
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization Emilio Gamba B. Bogaerts Tias Guns LRM 34 6 0 25 May 2021
Probabilistic Sufficient Explanations Eric Wang Pasha Khosravi Mathias Niepert XAI FAtt TPM 30 23 0 21 May 2021
SAT-Based Rigorous Explanations for Decision Lists Alexey Ignatiev Sasha Rubin XAI 25 44 0 14 May 2021
On Guaranteed Optimal Robust Explanations for NLP Models Emanuele La Malfa A. Zbrzezny Rhiannon Michelmore Nicola Paoletti Marta Z. Kwiatkowska FAtt 11 46 0 08 May 2021
On the Computational Intelligibility of Boolean Classifiers Gilles Audemard S. Bellart Louenas Bounia F. Koriche Jean-Marie Lagniez Pierre Marquis 19 56 0 13 Apr 2021
Declarative Approaches to Counterfactual Explanations for Classification Leopoldo Bertossi 37 17 0 15 Nov 2020
Abduction and Argumentation for Explainable Machine Learning: A Position Survey A. Kakas Loizos Michael 9 11 0 24 Oct 2020
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 24 85 0 21 Oct 2020
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Sasha Rubin Thomas Gerspacher Martin C. Cooper Alexey Ignatiev Nina Narodytska FAtt 24 58 0 13 Aug 2020
xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems Vineel Nagisetty Laura Graves Joseph Scott Vijay Ganesh GAN DRL 18 27 0 24 Feb 2020
On The Reasons Behind Decisions Adnan Darwiche Auguste Hirth FaML 17 145 0 21 Feb 2020
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 60 195 0 06 Apr 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 240 1,837 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016