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].
Adaptive Sample Sharing for Multi Agent Linear Bandits
Authors: Hamza Cherkaoui, Merwan Barlier, Igor Colin
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our main result formalizes the trade-off between the bias and uncertainty of the bandit parameter estimation for efficient collaboration. This result is the cornerstone of the Bandit Adaptive Sample Sharing (BASS) algorithm, whose efficiency over the current state-of-the-art is validated through both theoretical analysis and empirical evaluations on both synthetic and real-world datasets. |
| Researcher Affiliation | Collaboration | 1Huawei Noah s Ark Lab, France 2LTCI, Télécom Paris, Institut Polytechnique de Paris, France. Correspondence to: Hamza Cherkaoui <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Bandit Adaptive Sample Sharing (BASS) algorithm |
| Open Source Code | Yes | The code is publicly available and can be found at this repository. |
| Open Datasets | Yes | We consider two public datasets of ranking scenarios: Movie Lens and Yahoo! dataset. |
| Dataset Splits | No | The paper describes synthetic data generation and preprocessing for real datasets (Movie Lens and Yahoo!) but does not specify explicit training, validation, or test dataset splits in terms of percentages, sample counts, or predefined split references for reproducibility. |
| Hardware Specification | Yes | The experiments were run in Python on 50 Intel Xeon @ 3.20 GHz CPUs and lasted a week. |
| Software Dependencies | No | The paper mentions that experiments were run in Python, but it does not provide specific version numbers for Python itself or any other software libraries or dependencies used (e.g., PyTorch, TensorFlow, scikit-learn). |
| Experiment Setup | Yes | For each algorithm, we line-search the α parameter. We set our hyperparameter γ to 2 to match Gilitschenski & Hanebeck (2012). For the BASS algorithm, we test two values for δ {0.1, 0.9}. |