Online Control with Adversarial Disturbances
Author(s): Agarwal, Naman; Bullins, Brian; Hazan, Elad; Kakade, Sham; Singh, Karan
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Agarwal, Naman | - |
| dc.contributor.author | Bullins, Brian | - |
| dc.contributor.author | Hazan, Elad | - |
| dc.contributor.author | Kakade, Sham | - |
| dc.contributor.author | Singh, Karan | - |
| dc.date.accessioned | 2021-10-08T19:49:35Z | - |
| dc.date.available | 2021-10-08T19:49:35Z | - |
| dc.date.issued | 2019 | en_US |
| dc.identifier.citation | Agarwal, Naman, Brian Bullins, Elad Hazan, Sham Kakade, and Karan Singh. "Online Control with Adversarial Disturbances." In Proceedings of the 36th International Conference on Machine Learning (2019): pp. 111-119. | en_US |
| dc.identifier.issn | 2640-3498 | - |
| dc.identifier.uri | http://proceedings.mlr.press/v97/agarwal19c/agarwal19c.pdf | - |
| dc.identifier.uri | http://arks.princeton.edu/ark:/88435/pr1hz7d | - |
| dc.description.abstract | We study the control of linear dynamical systems with adversarial disturbances, as opposed to statistical noise. We present an efficient algorithm that achieves nearly-tight regret bounds in this setting. Our result generalizes upon previous work in two main aspects: the algorithm can accommodate adversarial noise in the dynamics, and can handle general convex costs. | en_US |
| dc.format.extent | 111 - 119 | en_US |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartof | Proceedings of the 36th International Conference on Machine Learning | en_US |
| dc.rights | Final published version. Article is made available in OAR by the publisher's permission or policy. | en_US |
| dc.title | Online Control with Adversarial Disturbances | en_US |
| dc.type | Conference Article | en_US |
| pu.type.symplectic | http://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceeding | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| OnlineControlAdversarialDisturbances.pdf | 275.69 kB | Adobe PDF | View/Download |
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