Foundations of Symbolic Languages for Model Interpretability

5 October 2021

Bernardo Subercaseaux

ReLM

LRM

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Papers citing "Foundations of Symbolic Languages for Model Interpretability"

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Title
Seeing Through Risk: A Symbolic Approximation of Prospect Theory Ali Arslan Yousaf Umair Rehman Muhammad Umair Danish 30 0 0 20 Apr 2025
On the Complexity of Global Necessary Reasons to Explain Classification M. Calautti Enrico Malizia Cristian Molinaro FAtt 63 0 0 12 Jan 2025
A Theoretical Survey on Foundation Models Shi Fu Yuzhu Chen Yingjie Wang Dacheng Tao 26 0 0 15 Oct 2024
Query languages for neural networks Martin Grohe Christoph Standke Juno Steegmans Jan Van den Bussche NAI 19 1 0 19 Aug 2024
Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation Guy Amir Shahaf Bassan Guy Katz 42 2 0 07 Aug 2024
Local vs. Global Interpretability: A Computational Complexity Perspective Shahaf Bassan Guy Amir Guy Katz 35 6 0 05 Jun 2024
Even-if Explanations: Formal Foundations, Priorities and Complexity Gianvincenzo Alfano S. Greco Domenico Mandaglio Francesco Parisi Reza Shahbazian I. Trubitsyna 26 2 0 17 Jan 2024
A Uniform Language to Explain Decision Trees Marcelo Arenas Pablo Barceló Diego Bustamante Jose Caraball Bernardo Subercaseaux 11 0 0 18 Oct 2023
On Formal Feature Attribution and Its Approximation Jinqiang Yu Alexey Ignatiev Peter James Stuckey 20 7 0 07 Jul 2023
On Logic-Based Explainability with Partially Specified Inputs Ramón Béjar António Morgado Jordi Planes João Marques-Silva 32 0 0 27 Jun 2023
Disproving XAI Myths with Formal Methods -- Initial Results João Marques-Silva 35 8 0 13 May 2023
Neurosymbolic AI and its Taxonomy: a survey Wandemberg Gibaut Leonardo Pereira Fabio Grassiotto Alexandre Osorio Eder Gadioli Amparo Munoz Sildolfo Gomes Claudio dos Santos NAI AI4CE 27 5 0 12 May 2023
COMET: Neural Cost Model Explanation Framework Isha Chaudhary Alex Renda Charith Mendis Gagandeep Singh 19 2 0 14 Feb 2023
Feature Necessity & Relevancy in ML Classifier Explanations Xuanxiang Huang Martin C. Cooper António Morgado Jordi Planes João Marques-Silva FAtt 30 18 0 27 Oct 2022
Logic-Based Explainability in Machine Learning João Marques-Silva LRM XAI 39 39 0 24 Oct 2022
On Computing Probabilistic Explanations for Decision Trees Marcelo Arenas Pablo Barceló M. Romero Bernardo Subercaseaux FAtt 29 38 0 30 Jun 2022
Eliminating The Impossible, Whatever Remains Must Be True Jinqiang Yu Alexey Ignatiev Peter James Stuckey Nina Narodytska João Marques-Silva 9 22 0 20 Jun 2022
On Tackling Explanation Redundancy in Decision Trees Yacine Izza Alexey Ignatiev João Marques-Silva FAtt 48 57 0 20 May 2022
Model Interpretability through the Lens of Computational Complexity Pablo Barceló Mikaël Monet Jorge A. Pérez Bernardo Subercaseaux 116 94 0 23 Oct 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,203 0 23 Aug 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017