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].
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. |