Risk-Sensitive Online Algorithms
We study the design of risk-sensitive online algorithms, in which risk measures are used in the competitive analysis of randomized online algorithms.
Authors
- Adam Wierman (ORCID: https://orcid.org/0000-0002-5923-0199)
- Steven H. Low (ORCID: https://orcid.org/0000-0001-6476-3048)
- Nicolas Christianson (ORCID: https://orcid.org/0000-0001-8330-8964)
- Bo Sun (ORCID: https://orcid.org/0000-0003-3172-7811)
Institutions
- California Institute of Technology (US)
- University of Waterloo (CA)
Publication Details
- Journal
- ACM SIGMETRICS Performance Evaluation Review
- Published
- 2024-09-05
- DOI
- https://doi.org/10.1145/3695411.3695415
- Citations
- 1
- Primary Topic
- Optimization and Search Problems
- Type
- article
- Field-Weighted Citation Impact
- 0.35