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 [1].
A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits
Authors: Huozhi Zhou, Lingda Wang, Lav Varshney, Ee-Peng Lim6933-6940
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on both synthetic and realworld datasets demonstrate the superiority of GLR-CUCB compared to other state-of-the-art algorithms. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign 2School of Information Systems, Singapore Management University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Sub-Bernoulli GLR Change-Point Detector: GLR(X1, , Xn; δ)... Algorithm 2 The GLR-CUCB Algorithm |
| Open Source Code | No | The paper does not provide a direct link or explicit statement that the source code for their proposed method (GLR-CUCB) is open-source or available. It only mentions that a baseline algorithm's code was provided by another researcher. |
| Open Datasets | Yes | We adopt the benchmark dataset for the real-world evaluation of bandit algorithms from Yahoo!. ... Yahoo! Front Page Today Module User Click Log Dataset on https://webscope.sandbox.yahoo.com |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, and testing. It mentions using 'synthetic and real-world datasets' but doesn't detail their splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In the synthetic dataset experiments: 'We let T = 5000, K = 6, m = 2, and N = 5.' In the Yahoo! Dataset experiments: 'To make the experiment nontrivial, we modify the dataset by: 1) the click rate of each base arm is enlarged by 10 times; 2) Reducing the time horizon to T = 22500.' Also, 'The details about parameter tuning for all of these algorithms for different experiments are included in Appendix C.2.' |