The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Introduction to Online Convex Optimization
Open accessAbstract
This manuscript portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed. This view of optimization as a process has become prominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.
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Cites
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An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
Communication-efficient distributed learning: An overview
Cao, X., Başar, T., Diggavi, S., Eldar, Y. C., Letaief, K. B., Poor, H. V., & Zhang, J. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3242710 · Cited by 139
An improved vehicle to the grid method with battery longevity management in a microgrid application
Yang, Q., Li, J., Cao, W., Li, S., Lin, J., Huo, D., & He, H. (2020) · Energy · doi:10.1016/j.energy.2020.117374 · Cited by 54
Online Learning of Unknown Dynamics for Model-Based Controllers in Legged Locomotion
Sun, Y., Ubellacker, W. L., Ma, W.-L., Zhang, X., Wang, C., Csomay-Shanklin, N. V., Tomizuka, M., Sreenath, K., & Ames, A. D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3108510 · Cited by 54
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions
Lekeufack, J., Angelopoulos, A. N., Bajcsy, A., Jordan, M. I., & Malik, J. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610041 · Cited by 18
Metadata
- Authors
- Hazan, E.
- Venue
- Foundations and Trends in Optimization
- Publisher
- Emerald
- Published
- 2016-08-30
- Volume
- 2
- Issue
- 3-4
- Pages
- 157-325
- ISSN
- 2167-3888 (ISSN-L)
- 2167-3918
- OpenAlex
- W2973115103
- Cited by
- 747
- References
- 75
- FWCI
- 44.16
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
