Name
Abstract | ||
|---|---|---|
We considered a novel practical problem of online learning with episodically revealed rewards, motivated by several real-world applications, where the contexts are nonstationary over different episodes and the reward feedbacks are not always available to the decision making agents. For this online semi-supervised learning setting, we introduced Background Episodic Reward LinUCB (BerlinUCB), a solution that easily incorporates clustering as a self-supervision module to provide useful side information when rewards are not observed. Our experiments on a variety of datasets, both in stationary and nonstationary environments of six different scenarios, demonstrated clear advantages of the proposed approach over the standard contextual bandit. Lastly, we introduced a relevant real-life example where this problem setting is especially useful. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-64984-5_32 | Australasian Conference on Artificial Intelligence |
DocType | Citations | PageRank |
Conference | 2 | 0.40 |
References | Authors | |
0 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baihan Lin | 1 | 2 | 4.11 |