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A General Theoretical Framework for Learning Smallest Interpretable Models
Artificial Intelligence
, volume 350, pages 104441, 2026.
Stefan Szeider
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DOI
Backdoors to Satisfaction Continued
Computer Science Review
, volume 60, pages 100868, 2026.
Stefan Szeider
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DOI
Graph Choosability via SAT: Beyond the Nullstellensatz
The 40th Annual AAAI Conference on Artificial Intelligence, AAAI-2026
, 2026.
Note: To appear
Markus Kirchweger
,
Tomas Peitl
,
David Seka
,
Stefan Szeider
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OBDDs, SDDs, and Circuits of Bounded Width: Completeness Matters
Artificial Intelligence
, volume 351, pages 104458, 2026.
Stefan Szeider
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Breaking Symmetries in Quantified Graph Search: A Comparative Study
AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA
(Toby Walsh and Julie Shah and Zico Kolter), pages 11246-11254, 2025, AAAI Press.
Markus Kirchweger
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Generating Streamlining Constraints with Large Language Models
Journal of Artificial Intelligence Research
, volume 84, pages 16:1-16:19, 2025.
Florentina Voboril
,
Vaidyanathan Peruvemba Ramaswamy
,
Stefan Szeider
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SAT Modulo Symmetries: A Survey
Satisfiability Checking and Symbolic Computation, 10th International Workshop, SC-Square 2025, Stuttgart, Germany, August 2, 2025, co-located with CADE 2025
, volume 4116 of
CEUR Workshop Proceedings
, pages 1-11, 2025, CEUR-WS.org.
Stefan Szeider
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StreamLLM: Enhancing Constraint Programming with Large Language Model-Generated Streamliners
2025 IEEE/ACM 1st International Workshop on Neuro-Symbolic Software Engineering (NSE)
, pages 17-22, 5 2025, IEEE.
Florentina Voboril
,
Peruvemba Ramaswamy, Vaidyanathan
,
Stefan Szeider
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DOI
The 3-Decomposition Conjecture: A SAT-Based Approach with Specialized Propagators
31st International Conference on Principles and Practice of Constraint Programming, CP 2025, August 10-15, 2025, Glasgow, Scotland
(Maria Garcia de la Banda), volume 340 of
LIPIcs
, pages 39:1-39:19, 2025, Schloss Dagstuhl - Leibniz-Zentrum für Informatik.
Stefan Szeider
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A General Theoretical Framework for Learning Smallest Interpretable Models
AAAI'24, the Thirty-Eighth AAAI Conference on Artificial Intelligence, February 20-27, Vancouver, Canada
(Jennifer Dy and Sriraam Natarajan), pages 10662-10669, 2024, AAAI Press.
Sebastian Ordyniak
,
Giacomo Paesani
,
Mateusz Rychlicki
,
Stefan Szeider
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