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ORCID21st CPAIOR 2024: Uppsala, Sweden - Part II
- Bistra Dilkina
:
Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 21st International Conference, CPAIOR 2024, Uppsala, Sweden, May 28-31, 2024, Proceedings, Part II. Lecture Notes in Computer Science 14743, Springer 2024, ISBN 978-3-031-60601-4 - Christoph Jabs, Jeremias Berg, Matti Järvisalo:
Core Boosting in SAT-Based Multi-objective Optimization. 1-19 - Connor Lawless, Oktay Günlük:
Fair Minimum Representation Clustering. 20-37 - Matthew J. McIlree, Ciaran McCreesh, Jakob Nordström:
Proof Logging for the Circuit Constraint. 38-55 - Gioni Mexi, Somayeh Shamsi, Mathieu Besançon, Pierre Le Bodic:
Probabilistic Lookahead Strong Branching via a Stochastic Abstract Branching Model. 56-73 - Mohsen Nafar, Michael Römer:
Lookahead, Merge and Reduce for Compiling Relaxed Decision Diagrams for Optimization. 74-82 - Rahul Patel, Elias B. Khalil:
LEO: Learning Efficient Orderings for Multiobjective Binary Decision Diagrams. 83-110 - Felipe de Carvalho Pereira, Pedro J. de Rezende, Tallys H. Yunes:
Minimizing the Cost of Leveraging Influencers in Social Networks: IP and CP Approaches. 111-127 - Egon Persak, Miguel F. Anjos:
Learning Deterministic Surrogates for Robust Convex QCQPs. 128-140 - Zhongdi Qu, Marc Grimson, Yue Mao, Sebastian Heilpern, Imanol Miqueleiz, Felipe Siqueira Pacheco, Alexander Flecker, Carla P. Gomes:
Strategies for Compressing the Pareto Frontier: Application to Strategic Planning of Hydropower in the Amazon Basin. 141-157 - Noah Schutte, Krzysztof Postek, Neil Yorke-Smith:
Improving Metaheuristic Efficiency for Stochastic Optimization by Sequential Predictive Sampling. 158-175 - Ajdin Sumic, Alessandro Cimatti, Andrea Micheli, Thierry Vidal:
SMT-Based Repair of Disjunctive Temporal Networks with Uncertainty: Strong and Weak Controllability. 176-192 - Bo Tang, Elias B. Khalil:
CaVE: A Cone-Aligned Approach for Fast Predict-then-optimize with Binary Linear Programs. 193-210 - Charles Thomas, Pierre Schaus:
A Constraint Programming Approach for Aircraft Disassembly Scheduling. 211-220 - Jiatai Tong, Junyang Cai, Thiago Serra:
Optimization over Trained Neural Networks: Taking a Relaxing Walk. 221-233 - Kim van den Houten, David M. J. Tax, Esteban Freydell, Mathijs de Weerdt:
Learning from Scenarios for Repairable Stochastic Scheduling. 234-242 - Andrea Visentin, Aodh Ó Gallchóir, Jens Kärcher, Herbert Meyr:
Explainable Algorithm Selection for the Capacitated Lot Sizing Problem. 243-252 - Bastián Véjar, Gaël Aglin, Ali Irfan Mahmutogullari, Siegfried Nijssen, Pierre Schaus, Tias Guns:
An Efficient Structured Perceptron for NP-Hard Combinatorial Optimization Problems. 253-262 - Adrian Wurm:
Robustness Verification in Neural Networks. 263-278 - Chao Yin, Quentin Cappart, Gilles Pesant:
An Improved Neuro-Symbolic Architecture to Fine-Tune Generative AI Systems. 279-288 - Haoruo Zhao, Hassan L. Hijazi, Haydn Jones, Juston Moore, Mathieu Tanneau, Pascal Van Hentenryck:
Bound Tightening Using Rolling-Horizon Decomposition for Neural Network Verification. 289-303 - Mehdi Zouitine, Ahmad Berjaoui, Agnès Lagnoux, Clément Pellegrini, Emmanuel Rachelson:
Learning Heuristics for Combinatorial Optimization Problems on K-Partite Hypergraphs. 304-314
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