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BatFix: Repairing language model-based transpilation

ACM transactions on software engineering and methodology, 2024-07, Vol.33 (6), p.1-29, Article 161

2024

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SAT Encodings and Beyond (Dagstuhl Seminar 23261)

2024

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Iterative Train Scheduling under Disruption with Maximum Satisfiability

The Journal of artificial intelligence research, 2024-01, Vol.79, p.1047-1090

2024

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MELT: Mining Effective Lightweight Transformations from Pull Requests

2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2023, p.1516-1528

2023

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DeepData: Machine learning in the marine ecosystems

Expert systems with applications, 2022-11, Vol.206, p.117841, Article 117841

2022

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Introducing UniCorT: an iterative university course timetabling tool with MaxSAT

Journal of scheduling, 2022-08, Vol.25 (4), p.371-390

2022

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Disruptions in timetables: a case study at Universidade de Lisboa

Journal of scheduling, 2021-02, Vol.24 (1), p.35-48

2021

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SOAR: A Synthesis Approach for Data Science API Refactoring

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), 2021, p.112-124

2021

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Replication of SOAR: A Synthesis Approach for Data Science API Refactoring

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 2021, p.190-191

2021

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UnchartIt: an interactive framework for program recovery from charts

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2020, p.175-186

2020

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ModRev - Model Revision Tool for Boolean Logical Models of Biological Regulatory Networks

Computational Methods in Systems Biology, p.339-348

Volltextzugriff (PDF)

Minimal Perturbation in University Timetabling with Maximum Satisfiability

Integration of Constraint Programming, Artificial Intelligence, and Operations Research, p.317-333

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QRAT Polynomially Simulates \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\forall \text {-Exp+Res}$$\end{document}

Theory and Applications of Satisfiability Testing – SAT 2019, 2019, p.193-202

2019

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documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathsf {CrystalBall}$$\end{document}: Gazing in the Black Box of SAT Solving

Theory and Applications of Satisfiability Testing – SAT 2019, 2019, p.371-387

2019

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Virtual machine consolidation using constraint-based multi-objective optimization

Journal of heuristics, 2019-06, Vol.25 (3), p.339-375

2019

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Derivation and external validation of the SHIeLD score for predicting outcome in normotensive pulmonary embolism

International journal of cardiology, 2019-04, Vol.281, p.119-124

2019

Volltextzugriff (PDF)

Repairing Boolean logical models from time-series data using Answer Set Programming

Algorithms for molecular biology, 2019-03, Vol.14 (1), p.9-9, Article 9

2019

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QRATPre+: Effective QBF Preprocessing via Strong Redundancy Properties

Theory and Applications of Satisfiability Testing - SAT 2019, 2019, Vol.11628, p.203-210

2019

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Speeding Up Assumption-Based SAT

Theory and Applications of Satisfiability Testing - SAT 2019, 2019, Vol.11628, p.164-182

2019

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Theory and Applications of Satisfiability Testing - SAT 2019: 22nd International Conference, SAT 2019, Lisbon, Portugal, July 9-12, 2019, Proceedings

1st Edition 2019, 2019

Volltextzugriff (PDF)

Backing Backtracking

Theory and Applications of Satisfiability Testing - SAT 2019, 2019, Vol.11628, p.250-266

2019

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Guiding High-Performance SAT Solvers with Unsat-Core Predictions

Theory and Applications of Satisfiability Testing - SAT 2019, 2019, Vol.11628, p.336-353

2019

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Local Search for Fast Matrix Multiplication

Theory and Applications of Satisfiability Testing - SAT 2019, 2019, Vol.11628, p.155-163

2019

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On Super Strong ETH

Theory and Applications of Satisfiability Testing - SAT 2019, 2019, Vol.11628, p.406-423

2019

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Assessing Heuristic Machine Learning Explanations with Model Counting

Theory and Applications of Satisfiability Testing - SAT 2019, 2019, Vol.11628, p.267-278

2019

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BatFix: Repairing language model-based transpilation

SAT Encodings and Beyond (Dagstuhl Seminar 23261)

Iterative Train Scheduling under Disruption with Maximum Satisfiability

MELT: Mining Effective Lightweight Transformations from Pull Requests

DeepData: Machine learning in the marine ecosystems

Introducing UniCorT: an iterative university course timetabling tool with MaxSAT

Disruptions in timetables: a case study at Universidade de Lisboa

SOAR: A Synthesis Approach for Data Science API Refactoring

Replication of SOAR: A Synthesis Approach for Data Science API Refactoring

UnchartIt: an interactive framework for program recovery from charts

ModRev - Model Revision Tool for Boolean Logical Models of Biological Regulatory Networks

Minimal Perturbation in University Timetabling with Maximum Satisfiability

QRAT Polynomially Simulates \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\forall \text {-Exp+Res}$$\end{document}

Virtual machine consolidation using constraint-based multi-objective optimization

Derivation and external validation of the SHIeLD score for predicting outcome in normotensive pulmonary embolism

Repairing Boolean logical models from time-series data using Answer Set Programming

QRATPre+: Effective QBF Preprocessing via Strong Redundancy Properties

Speeding Up Assumption-Based SAT

Theory and Applications of Satisfiability Testing - SAT 2019: 22nd International Conference, SAT 2019, Lisbon, Portugal, July 9-12, 2019, Proceedings

Backing Backtracking

Guiding High-Performance SAT Solvers with Unsat-Core Predictions

Local Search for Fast Matrix Multiplication

On Super Strong ETH

Assessing Heuristic Machine Learning Explanations with Model Counting

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