Multinomial Logit Bandit with Low Switching Cost

@InProceedings{pmlr-v119-dong20b,
  title = 	 {Multinomial Logit Bandit with Low Switching Cost},
  author =       {Dong, Kefan and Li, Yingkai and Zhang, Qin and Zhou, Yuan},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {2607--2615},
  year = 	 {2020},
  editor = 	 {III, Hal Daumé and Singh, Aarti},
  volume = 	 {119},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--18 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v119/dong20b/dong20b.pdf},
  url = 	 {https://proceedings.mlr.press/v119/dong20b.html},
  abstract = 	 {We study multinomial logit bandit with limited adaptivity, where the algorithms change their exploration actions as infrequently as possible when achieving almost optimal minimax regret. We propose two measures of adaptivity: the assortment switching cost and the more fine-grained item switching cost. We present an anytime algorithm (AT-DUCB) with $O(N \log T)$ assortment switches, almost matching the lower bound $\Omega(\frac{N \log T}{ \log \log T})$. In the fixed-horizon setting, our algorithm FH-DUCB incurs $O(N \log \log T)$ assortment switches, matching the asymptotic lower bound. We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.}
}