Combinatorial Causal Bandits
Authors: Shi Feng, Wei Chen
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate effectiveness of our algorithms by experimental evaluations in appendix. |
| Researcher Affiliation | Collaboration | 1 Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China 2 Microsoft Research, Beijing, China |
| Pseudocode | Yes | Algorithm 1: BGLM-OFU for BGLM CCB Problem; Algorithm 2: BGLM-Estimate; Algorithm 3: BLM-LR for BLM CCB Problem |
| Open Source Code | No | The paper states, 'Due to space limits, our appendix is included in the full version (Feng and Chen 2022) on ar Xiv.', which refers to a pre-print version, not an explicit code repository or statement of code release for the methodology. |
| Open Datasets | No | The paper mentions 'experimental evaluations in appendix' but does not provide specific dataset names, links, or citations to publicly available datasets within the main text. |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset split information. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values or training configurations. |