DagRep.13.2.1.pdf
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Scheduling (Dagstuhl Seminar 23061)
Authors and all authors of the abstracts in this report
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Issue:
Dagstuhl Reports, Volume 13, Issue 2
Volume: Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-10-09
Cite AsGet BibTex
Nicole Megow, Benjamin J. Moseley, David Shmoys, Ola Svensson, Sergei Vassilvitskii, and Jens Schlöter. Scheduling (Dagstuhl Seminar 23061). In Dagstuhl Reports, Volume 13, Issue 2, pp. 1-19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/DagRep.13.2.1
https://doi.org/10.4230/DagRep.13.2.1
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23061 "Scheduling". The seminar focused on the emerging models for beyond-worst case algorithm design, in particular, recent approaches that incorporate learning. This includes models for the integration of learning into algorithm design that have been proposed recently and that have already demonstrated advances in the state-of-art for various scheduling applications: (i) scheduling with error-prone learned predictions, (ii) data-driven algorithm design, and (iii) stochastic and Bayesian learning in scheduling.
Subject Classification
ACM Subject Classification
- Mathematics of computing → Combinatorial optimization
- Theory of computation → Approximation algorithms analysis
Keywords
- scheduling
- mathematical optimization
- approximation algorithms
- learning methods
- uncertainty
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