Suchergebnisse - "data-driven algorithm design"

How Much Data Is Sufficient to Learn High-Performing Algorithms?: How much data is sufficient to learn high-performing algorithms?

Autoren: Maria-Florina Balcan Dan Deblasio Travis Dick et al.

Quelle: Journal of the ACM. 71:1-58

Schlagwörter: 0301 basic medicine, data-driven algorithm design, 0303 health sciences, 03 medical and health sciences, machine learning, computational biology, General topics in the theory of algorithms, Learning and adaptive systems in artificial intelligence, automated algorithm design, automated algorithm configuration

Dateibeschreibung: application/xml

Data-Driven Solution Portfolios

Autoren: Drygala, Marina Lattanzi, Silvio Maggiori, Andreas et al.

Weitere Verfasser: Drygala, Marina Lattanzi, Silvio Maggiori, Andreas et al.

Schlagwörter: FOS: Computer and information sciences, data-driven algorithm design, Computer Science - Data Structures and Algorithms, solution portfolios, Data Structures and Algorithms (cs.DS), ddc:004, matroids

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2412.00717
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.46

Data-Driven Solution Portfolios

Autoren: Drygala, Marina Lattanzi, Silvio Maggiori, Andreas et al.

Schlagwörter: data-driven algorithm design, matroids, solution portfolios

Relation: 16th Innovations in Theoretical Computer Science Conference (ITCS 2025); Leibniz International Proceedings in Informatics (LIPIcs); 325; The 16th Annual Innovations in Theoretical Computer Science (ITCS) conference; #PLACEHOLDER_PARENT_METADATA_VALUE#; MB22.00054; https://infoscience.epfl.ch/handle/20.500.14299/247349

Verfügbarkeit: https://doi.org/10.4230/LIPIcs.ITCS.2025.46
https://infoscience.epfl.ch/handle/20.500.14299/247349
https://hdl.handle.net/20.500.14299/247349

Improved Sample Complexity Bounds for Branch-And-Cut

Autoren: Balcan, Maria-Florina Prasad, Siddharth Sandholm, Tuomas et al.

Weitere Verfasser: Balcan, Maria-Florina Prasad, Siddharth Sandholm, Tuomas et al.

Schlagwörter: Automated algorithm configuration, integer programming, machine learning theory, tree search, branch-and-bound, branch-and-cut, cutting planes, sample complexity, generalization guarantees, data-driven algorithm design

Dateibeschreibung: application/pdf

Relation: Is Part Of LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022); https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.3

Verfügbarkeit: https://doi.org/10.4230/LIPIcs.CP.2022.3
https://nbn-resolving.org/urn:nbn:de:0030-drops-166321
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.3

Improved Sample Complexity Bounds for Branch-and-Cut

Autoren: Balcan, Maria-Florina Prasad, Siddharth Sandholm, Tuomas et al.

Weitere Verfasser: Balcan, Maria-Florina Prasad, Siddharth Sandholm, Tuomas et al.

Schlagwörter: FOS: Computer and information sciences, sample complexity, data-driven algorithm design, Computer Science - Machine Learning, tree search, Automated algorithm configuration, machine learning theory, Computer Science - Artificial Intelligence, 0211 other engineering and technologies, 02 engineering and technology, cutting planes, Machine Learning (cs.LG), branch-and-cut, Artificial Intelligence (cs.AI), generalization guarantees, Optimization and Control (math.OC), Computer Science - Data Structures and Algorithms, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, branch-and-bound, Data Structures and Algorithms (cs.DS), ddc:004, integer programming, Mathematics - Optimization and Control

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2111.11207
https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.3

Learning-based search algorithm design

Autoren: Chen, Binghong Zhang, Chao Song, Le et al.

Weitere Verfasser: Chen, Binghong Zhang, Chao Song, Le et al.

Schlagwörter: Learning to search, Data-driven algorithm design, Deep learning, Reinforcement learning, Meta-learning, Graph representation learning, Structured prediction, Robotics applications, AI for drug discovery, Chemistry applications

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/1853/72742

Verfügbarkeit: https://hdl.handle.net/1853/72742

Automated Algorithm and Mechanism Configuration

Autoren: Ellen Vitercik

Schlagwörter: Applied computing not elsewhere classified, automated algorithm design, data-driven algorithm design, automated algorithm configuration, sample complexity, machine learning theory, mechanism design, Applied Computer Science

Relation: https://figshare.com/articles/thesis/Automated_Algorithm_and_Mechanism_Configuration/17207516

Verfügbarkeit: https://doi.org/10.1184/r1/17207516.v1
https://figshare.com/articles/thesis/Automated_Algorithm_and_Mechanism_Configuration/17207516

Learn to optimize-a brief overview.

Autoren: Tang K Yao X

Quelle: National science review [Natl Sci Rev] 2024 Apr 02; Vol. 11 (8), pp. nwae132. Date of Electronic Publication: 2024 Apr 02 (Print Publication: 2024).

Publikationsart: Journal Article; Review

Info zur Zeitschrift: Publisher: China Science Publishing Country of Publication: China NLM ID: 101633095 Publication Model: eCollection Cited Medium: Internet ISSN: 2053-714X (Electronic) Linking ISSN: 2053714X NLM ISO Abbreviation: Natl Sci Rev Subsets: PubMed not MEDLINE

Improved Sample Complexity Bounds for Branch-And-Cut

Autoren: Maria-Florina Balcan and Siddharth Prasad and Tuomas Sandholm and Ellen Vitercik Balcan, Maria-Florina Prasad, Siddharth et al.

Index Begriffe: Automated algorithm configuration, integer programming, machine learning theory, tree search, branch-and-bound, branch-and-cut, cutting planes, sample complexity, generalization guarantees, data-driven algorithm design, InProceedings, Text, doc-type:ResearchArticle, publishedVersion

URL: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.3
Is Part Of LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)