Name
Abstract | ||
|---|---|---|
We study bandit convex optimization methods that adapt to the norm of the comparator, a topic that has only been studied before for its full-information counterpart. Specifically, we develop convex bandit algorithms with regret bounds that are small whenever the norm of the comparator is small. We first use techniques from the full-information setting to develop comparator-adaptive algorithms for linear bandits. Then, we extend the ideas to convex bandits with Lipschitz or smooth loss functions, using a new single-point gradient estimator and carefully designed surrogate losses. |
Year | Venue | DocType |
|---|---|---|
2020 | NIPS 2020 | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dirk van der Hoeven | 1 | 0 | 2.37 |
| Cutkosky, Ashok | 2 | 14 | 10.02 |
| Haipeng Luo | 3 | 184 | 21.20 |