Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Combinatorial Causal Bandits
Authors: Shi Feng, Wei Chen
AAAI 2023 | Venue PDF | 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. |