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..
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits
Authors: Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now evaluate the empirical performance of Low Pop Art and our proposed experimental design to validate our improvement. |
| Researcher Affiliation | Academia | 1Dipartimento di Informatica, Università degli Studi di Milano, Milan, MI, Italy 2Department of Computer Science, University of Arizona, Tucson, AZ, United States. |
| Pseudocode | Yes | Algorithm 1 Low Pop Art |
| Open Source Code | Yes | please check https://github.com/jajajang/Low Pop Art for the code. |
| Open Datasets | Yes | We used the Movielens dataset (movielens-old 100k) to try the algorithm on a real-world dataset. |
| Dataset Splits | No | No explicit mention of specific training, validation, or test dataset splits or cross-validation methodology. |
| Hardware Specification | Yes | Computation resource: Apple M2 Pro, 16GB memory. |
| Software Dependencies | No | Software like Scikit-learn and cvxpy are mentioned, but no specific version numbers are provided. |
| Experiment Setup | Yes | For all experiments, we set ground truth Θ = uv where , u Unif(Sd1 1) and v Unif(Sd2 1) and we sample Θ before each experiment starts. The noise of the reward ηt N(0, 1). All plots are generated by averaging over 60 number of random instances. |