Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness
Authors: Qingsong Liu, Weihang Xu, Siwei Wang, Zhixuan Fang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct numerical experiments to validate our theoretical results. Our experimental results are given in the suppmetary material. |
| Researcher Affiliation | Collaboration | 1 IIIS, Tsinghua University 2 Microsoft Research 3 Shanghai Qi Zhi Institute |
| Pseudocode | Yes | Algorithm 1 UCB-PLLP |
| Open Source Code | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | No | The paper does not provide information about specific datasets used, their public availability, or citations to them. |
| Dataset Splits | No | The paper discusses theoretical results and states experimental results are in supplementary material, but does not specify dataset splits for training, validation, or testing in the main text. |
| Hardware Specification | No | 3. If you ran experiments... (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper states experimental results are in supplementary material but does not provide specific experimental setup details like hyperparameters or training configurations in the main text. |