João Marques-Silva

ORCID

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 118

Top Venues

Publications

Olivier Letoffe, Xuanxiang Huang, Nicholas Asher, João Marques-Silva
From SHAP Scores to Feature Importance Scores. CoRR (2024)
João Marques-Silva
Logic-Based Explainability: Past, Present & Future. CoRR (2024)
Yacine Izza, Alexey Ignatiev, Peter J. Stuckey, João Marques-Silva
Delivering Inflated Explanations. AAAI (2024)
Olivier Letoffe, Xuanxiang Huang, João Marques-Silva
On Correcting SHAP Scores. CoRR (2024)
Yacine Izza, Xuanxiang Huang, António Morgado, Jordi Planes, Alexey Ignatiev, João Marques-Silva
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation. CoRR (2024)
Ismaïl Baaj, Zied Bouraoui, Antoine Cornuéjols, Thierry Denoeux, Sébastien Destercke, Didier Dubois, Marie-Jeanne Lesot, João Marques-Silva, Jérôme Mengin, Henri Prade, Steven Schockaert, Mathieu Serrurier, Olivier Strauss, Christel Vrain
Synergies between machine learning and reasoning - An introduction by the Kay R. Amel group. Int. J. Approx. Reason. 171 (2024)
Xuanxiang Huang, João Marques-Silva
On the failings of Shapley values for explainability. Int. J. Approx. Reason. 171 (2024)
Ramón Béjar, António Morgado, Jordi Planes, João Marques-Silva
On Logic-Based Explainability with Partially Specified Inputs. CoRR (2023)
Aurélie Hurault, João Marques-Silva
Certified Logic-Based Explainable AI - The Case of Monotonic Classifiers. TAP (2023)
Clément Carbonnel, Martin C. Cooper, João Marques-Silva
Tractable Explaining of Multivariate Decision Trees. KR (2023)
Xuanxiang Huang, João Marques-Silva
From Decision Trees to Explained Decision Sets. ECAI (2023)
Yacine Izza, João Marques-Silva
On Computing Relevant Features for Explaining NBCs. ENIGMA@KR (2023)
Xuanxiang Huang, João Marques-Silva
From Robustness to Explainability and Back Again. CoRR (2023)
João Marques-Silva, Alexey Ignatiev
No silver bullet: interpretable ML models must be explained. Frontiers Artif. Intell. 6 (2023)
Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey, Nina Narodytska, João Marques-Silva
Eliminating the Impossible, Whatever Remains Must Be True: On Extracting and Applying Background Knowledge in the Context of Formal Explanations. AAAI (2023)
Yacine Izza, Alexey Ignatiev, João Marques-Silva
On Tackling Explanation Redundancy in Decision Trees (Extended Abstract). IJCAI (2023)
João Marques-Silva, Xuanxiang Huang
Explainability is NOT a Game. CoRR (2023)
Yacine Izza, João Marques-Silva
The Pros and Cons of Adversarial Robustness. CoRR (2023)
Raúl Mencía, Carlos Mencía, João Marques-Silva
Efficient Reasoning about Infeasible One Machine Sequencing. ICAPS (2023)
Xuanxiang Huang, João Marques-Silva
A Refutation of Shapley Values for Explainability. CoRR (2023)
Xuanxiang Huang, Yacine Izza, João Marques-Silva
Solving Explainability Queries with Quantification: The Case of Feature Relevancy. AAAI (2023)
Yacine Izza, Xuanxiang Huang, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, João Marques-Silva
On computing probabilistic abductive explanations. Int. J. Approx. Reason. 159 (2023)
Yacine Izza, Alexey Ignatiev, Peter J. Stuckey, João Marques-Silva
Delivering Inflated Explanations. CoRR (2023)
Xuanxiang Huang, Martin C. Cooper, António Morgado, Jordi Planes, João Marques-Silva
Feature Necessity & Relevancy in ML Classifier Explanations. TACAS (1) (2023)
Xuanxiang Huang, João Marques-Silva
Refutation of Shapley Values for XAI - Additional Evidence. CoRR (2023)
Xuanxiang Huang, João Marques-Silva
The Inadequacy of Shapley Values for Explainability. CoRR (2023)
Yacine Izza, Kuldeep S. Meel, João Marques-Silva
Locally-Minimal Probabilistic Explanations. CoRR (2023)
Xuanxiang Huang, Martin C. Cooper, António Morgado, Jordi Planes, João Marques-Silva
Feature Necessity & Relevancy in ML Classifier Explanations. CoRR (2022)
Alexey Ignatiev, Yacine Izza, Peter J. Stuckey, João Marques-Silva
Using MaxSAT for Efficient Explanations of Tree Ensembles. AAAI (2022)
Yacine Izza, Alexey Ignatiev, João Marques-Silva
On Tackling Explanation Redundancy in Decision Trees. J. Artif. Intell. Res. 75 (2022)
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev, Martin C. Cooper, Nicholas Asher, João Marques-Silva
Tractable Explanations for d-DNNF Classifiers. AAAI (2022)
Yacine Izza, Xuanxiang Huang, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, João Marques-Silva
On Computing Probabilistic Abductive Explanations. CoRR (2022)
João Marques-Silva, Alexey Ignatiev
Delivering Trustworthy AI through Formal XAI. AAAI (2022)
Yacine Izza, João Marques-Silva
On Computing Relevant Features for Explaining NBCs. CoRR (2022)
Yacine Izza, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, João Marques-Silva
Provably Precise, Succinct and Efficient Explanations for Decision Trees. CoRR (2022)
João Marques-Silva
Logic-Based Explainability in Machine Learning. CoRR (2022)
Yacine Izza, Alexey Ignatiev, João Marques-Silva
On Tackling Explanation Redundancy in Decision Trees. CoRR (2022)
Aditya A. Shrotri, Nina Narodytska, Alexey Ignatiev, Kuldeep S. Meel, João Marques-Silva, Moshe Y. Vardi
Constraint-Driven Explanations for Black-Box ML Models. AAAI (2022)
Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey, Nina Narodytska, João Marques-Silva
Eliminating The Impossible, Whatever Remains Must Be True. CoRR (2022)
Xuanxiang Huang, João Marques-Silva
On Deciding Feature Membership in Explanations of SDD & Related Classifiers. CoRR (2022)
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev, Martin C. Cooper, Nicholas Asher, João Marques-Silva
Efficient Explanations for Knowledge Compilation Languages. CoRR (2021)
Gianpiero Cabodi, Paolo E. Camurati, Alexey Ignatiev, João Marques-Silva, Marco Palena, Paolo Pasini
Optimizing Binary Decision Diagrams for Interpretable Machine Learning Classification. DATE (2021)
João Marques-Silva
Automated Reasoning in Explainable AI. CCIA (2021)
Yacine Izza, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper, João Marques-Silva
Efficient Explanations With Relevant Sets. CoRR (2021)
Takfarinas Saber, Anthony Ventresque, João Marques-Silva, James Thorburn, Liam Murphy
MILP for the Multi-objective VM Reassignment Problem. CoRR (2021)
João Marques-Silva, Rafael Peñaloza, Uli Sattler
Extending the Synergies Between SAT and Description Logics (Dagstuhl Seminar 21361). Dagstuhl Reports 11 (8) (2021)
João Marques-Silva, Thomas Gerspacher, Martin C. Cooper, Alexey Ignatiev, Nina Narodytska
Explanations for Monotonic Classifiers. CoRR (2021)
Yacine Izza, João Marques-Silva
On Explaining Random Forests with SAT. CoRR (2021)
Alexey Ignatiev, João Marques-Silva
SAT-Based Rigorous Explanations for Decision Lists. CoRR (2021)
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev, João Marques-Silva
On Efficiently Explaining Graph-Based Classifiers. KR (2021)
Daniel Gibert, Carles Mateu, Jordi Planes, João Marques-Silva
Auditing static machine learning anti-Malware tools against metamorphic attacks. Comput. Secur. 102 (2021)
Martin C. Cooper, João Marques-Silva
On the Tractability of Explaining Decisions of Classifiers. CP (2021)
João Marques-Silva, Thomas Gerspacher, Martin C. Cooper, Alexey Ignatiev, Nina Narodytska
Explanations for Monotonic Classifiers. ICML (2021)
Stepan Kochemazov, Alexey Ignatiev, João Marques-Silva
Assessing Progress in SAT Solvers Through the Lens of Incremental SAT. SAT (2021)
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev, João Marques-Silva
On Efficiently Explaining Graph-Based Classifiers. CoRR (2021)
Yacine Izza, João Marques-Silva
On Explaining Random Forests with SAT. IJCAI (2021)
Alexey Ignatiev, João Marques-Silva, Nina Narodytska, Peter J. Stuckey
Reasoning-Based Learning of Interpretable ML Models. IJCAI (2021)
Alexey Ignatiev, Edward Lam, Peter J. Stuckey, João Marques-Silva
A Scalable Two Stage Approach to Computing Optimal Decision Sets. AAAI (2021)
Alexey Ignatiev, Edward Lam, Peter J. Stuckey, João Marques-Silva
A Scalable Two Stage Approach to Computing Optimal Decision Sets. CoRR (2021)
Maria Luisa Bonet, Sam Buss, Alexey Ignatiev, António Morgado, João Marques-Silva
Propositional proof systems based on maximum satisfiability. Artif. Intell. 300 (2021)
Carlos Mencía, João Marques-Silva
Reasoning About Strong Inconsistency in ASP. SAT (2020)
Alexey Ignatiev, Nina Narodytska, Nicholas Asher, João Marques-Silva
From Contrastive to Abductive Explanations and Back Again. AI*IA (2020)
João Marques-Silva, Thomas Gerspacher, Martin C. Cooper, Alexey Ignatiev, Nina Narodytska
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay. CoRR (2020)
Oleg Zaikin, Alexey Ignatiev, João Marques-Silva
Branch Location Problems with Maximum Satisfiability. ECAI (2020)
João Marques-Silva, Carlos Mencía
Reasoning About Inconsistent Formulas. IJCAI (2020)
Mario Alviano, Carmine Dodaro, João Marques-Silva, Francesco Ricca
Optimum stable model search: algorithms and implementation. J. Log. Comput. 30 (4) (2020)
João Marques-Silva, Thomas Gerspacher, Martin C. Cooper, Alexey Ignatiev, Nina Narodytska
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay. NeurIPS (2020)
Alexey Ignatiev, Nina Narodytska, Nicholas Asher, João Marques-Silva
On Relating 'Why?' and 'Why Not?' Explanations. CoRR (2020)
Alexey Ignatiev, Martin C. Cooper, Mohamed Siala, Emmanuel Hebrard, João Marques-Silva
Towards Formal Fairness in Machine Learning. CP (2020)
Yacine Izza, Alexey Ignatiev, João Marques-Silva
On Explaining Decision Trees. CoRR (2020)
Carlos Mencía, Oliver Kullmann, Alexey Ignatiev, João Marques-Silva
On Computing the Union of MUSes. SAT (2019)
António Morgado, Alexey Ignatiev, Maria Luisa Bonet, João Marques-Silva, Sam Buss
DRMaxSAT with MaxHS: First Contact. SAT (2019)
Alexey Ignatiev, Nina Narodytska, João Marques-Silva
On Relating Explanations and Adversarial Examples. NeurIPS (2019)
Ilya Zakirzyanov, António Morgado, Alexey Ignatiev, Vladimir Ulyantsev, João Marques-Silva
Efficient Symmetry Breaking for SAT-Based Minimum DFA Inference. LATA (2019)
Alexey Ignatiev, Nina Narodytska, João Marques-Silva
Abduction-Based Explanations for Machine Learning Models. AAAI (2019)
Alexey Ignatiev, António Morgado, João Marques-Silva
RC2: an Efficient MaxSAT Solver. J. Satisf. Boolean Model. Comput. 11 (1) (2019)
Luís Cruz-Filipe, João Marques-Silva, Peter Schneider-Kamp
Formally Verifying the Solution to the Boolean Pythagorean Triples Problem. J. Autom. Reason. 63 (3) (2019)
Carlos Mencía, João Marques-Silva
Computing Shortest Resolution Proofs. EPIA (2) (2019)
Nina Narodytska, Aditya A. Shrotri, Kuldeep S. Meel, Alexey Ignatiev, João Marques-Silva
Assessing Heuristic Machine Learning Explanations with Model Counting. SAT (2019)
Alexey Ignatiev, António Morgado, Georg Weissenbacher, João Marques-Silva
Model-Based Diagnosis with Multiple Observations. IJCAI (2019)
Alexey Ignatiev, Nina Narodytska, João Marques-Silva
On Validating, Repairing and Refining Heuristic ML Explanations. CoRR (2019)
Zied Bouraoui, Antoine Cornuéjols, Thierry Denoeux, Sébastien Destercke, Didier Dubois, Romain Guillaume, João Marques-Silva, Jérôme Mengin, Henri Prade, Steven Schockaert, Mathieu Serrurier, Christel Vrain
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group). CoRR (2019)
Alexey Ignatiev, António Morgado, João Marques-Silva
PySAT: A Python Toolkit for Prototyping with SAT Oracles. SAT (2018)
Nina Narodytska, Alexey Ignatiev, Filipe Pereira, João Marques-Silva
Learning Optimal Decision Trees with SAT. IJCAI (2018)
Alessandro Previti, Carlos Mencía, Matti Järvisalo, João Marques-Silva
Premise Set Caching for Enumerating Minimal Correction Subsets. AAAI (2018)
Alexey Ignatiev, Filipe Pereira, Nina Narodytska, João Marques-Silva
A SAT-Based Approach to Learn Explainable Decision Sets. IJCAR (2018)
Maria Luisa Bonet, Sam Buss, Alexey Ignatiev, João Marques-Silva, António Morgado
MaxSAT Resolution With the Dual Rail Encoding. AAAI (2018)
Alexey Ignatiev, Nina Narodytska, João Marques-Silva
Abduction-Based Explanations for Machine Learning Models. CoRR (2018)
João Marques-Silva
Computing with SAT Oracles: Past, Present and Future. CiE (2018)
Alessandro Previti, Alexey Ignatiev, Matti Järvisalo, João Marques-Silva
On Computing Generalized Backbones. ICTAI (2017)
Alexey Ignatiev, António Morgado, João Marques-Silva
On Tackling the Limits of Resolution in SAT Solving. SAT (2017)
Mikolás Janota, João Marques-Silva
An Achilles' Heel of Term-Resolution. EPIA (2017)
João Marques-Silva, Alexey Ignatiev, António Morgado
Horn Maximum Satisfiability: Reductions, Algorithms & Applications. CoRR (2017)
Alessandro Previti, Carlos Mencía, Matti Järvisalo, João Marques-Silva
Improving MCS Enumeration via Caching. SAT (2017)
João Marques-Silva, Mikolás Janota, Carlos Mencía
Minimal sets on propositional formulae. Problems and reductions. Artif. Intell. 252 (2017)
Alexey Ignatiev, António Morgado, João Marques-Silva
Model Based Diagnosis of Multiple Observations with Implicit Hitting Sets. CoRR (2017)
Rafael Peñaloza, Carlos Mencía, Alexey Ignatiev, João Marques-Silva
Lean Kernels in Description Logics. ESWC (1) (2017)
Alexey Ignatiev, João Marques-Silva, Carlos Mencía, Rafael Peñaloza
Debugging EL+ Ontologies through Horn MUS Enumeration. Description Logics (2017)
Takfarinas Saber, João Marques-Silva, James Thorburn, Anthony Ventresque
Exact and Hybrid Solutions for the Multi-Objective VM Reassignment Problem. Int. J. Artif. Intell. Tools 26 (1) (2017)
Alexey Ignatiev, António Morgado, João Marques-Silva
On Tackling the Limits of Resolution in SAT Solving. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

João Marques-Silva