Non-stationary Bandits with Knapsacks
Authors: Shang Liu, Jiashuo Jiang, Xiaocheng Li
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments compare the performance of our algorithm with existing Bw K algorithms and are presented in Appendix A. |
| Researcher Affiliation | Academia | Imperial College Business School, Imperial College London NYU Stern School of Business s.liu21@imperial.ac.uk, jj2398@stern.nyu.edu, xiaocheng.li@imperial.ac.uk |
| Pseudocode | Yes | Algorithm 1 Sliding-Window UCB Algorithm for Bw K |
| Open Source Code | No | The paper mentions numerical experiments and an algorithm but does not provide any explicit statements about open-source code availability or links to repositories. |
| Open Datasets | No | The paper refers to "Numerical experiments" in Appendix A, but does not specify any publicly available datasets, provide links, or formal citations for them in the main text. |
| Dataset Splits | No | The paper mentions "Numerical experiments" but does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes Algorithm 1 but does not provide specific details about experimental setup, such as hyperparameters or system-level training settings. |