Delay and Cooperation in Nonstochastic Linear Bandits
Authors: Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper offers a nearly optimal algorithm for online linear optimization with delayed bandit feedback. ... This algorithm achieves nearly optimal performance, as we are able to show that arbitrary algorithms suffer the regret of ( m(m + d)T) in the worst case. To develop the algorithm, we introduce a technique we refer to as distribution truncation, which plays an essential role in bounding the regret. |
| Researcher Affiliation | Collaboration | Shinji Ito NEC Corporation i-shinji@nec.com Daisuke Hatano RIKEN AIP daisuke.hatano@riken.jp Hanna Sumita Tokyo Institute of Technology sumita@c.titech.ac.jp Kei Takemura NEC Corporation kei_takemura@nec.com Takuro Fukunaga Chuo University, RIKEN AIP, JST PRESTO fukunaga.07s@g.chuo-u.ac.jp Naonori Kakimura Keio University kakimura@math.keio.ac.jp Ken-ichi Kawarabayashi National Institute of Informatics k-keniti@nii.ac.jp |
| Pseudocode | Yes | Algorithm 1 An algorithm for online linear optimization with delayed bandit feedback |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper is a theoretical work and does not use datasets for training. Therefore, it does not provide information about publicly available datasets. |
| Dataset Splits | No | This paper is a theoretical work and does not use datasets for validation. Therefore, it does not provide information about dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the specific hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup or software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or system-level training settings. |