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..
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.' |