Name
Title | ||
|---|---|---|
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging |
Abstract | ||
|---|---|---|
Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection. In these applications, a `reneging' phenomenon, where participants may disengage from future interactions after observing an unsatisfiable outcome, is rather prevalent. To address the above issue, this paper proposes a model of heteroscedastic linear bandits with reneging, which allows each participant to have a distinct `satisfaction level,' with any interaction outcome falling short of that level resulting in that participant reneging. Moreover, it allows the variance of the outcome to be context-dependent. Based on this model, we develop a UCB-type policy, namely HR-UCB, and prove that it achieves \pch{$\mathcal{O}\big(\sqrt{{T}(\log({T}))^{3}}\big)$} regret. Finally, we validate the performance of HR-UCB via simulations. |
Year | Venue | Field |
|---|---|---|
2019 | international conference on machine learning | Mathematical optimization,Heteroscedasticity,Regret,Portfolio,Medical treatment,Phenomenon,Mathematics,Maximization |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping-Chun Hsieh | 1 | 16 | 7.01 |
| Xi Liu | 2 | 122 | 20.80 |
| anirban bhattacharya | 3 | 1 | 3.07 |
| P. R. Kumar | 4 | 7177 | 1067.24 |