Near-Optimal Collaborative Learning in Bandits
Authors: Clémence Réda, Sattar Vakili, Emilie Kaufmann
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose in Appendix E an empirical evaluation of W-CPE-BAI for the weight matrix wm,n = α1(n = m) + 1 α M which corresponds to the setting studied by [30]. Our experiments on a synthetic instance show that W-CPE-BAI and PF-UCB-BAI have similar performances in terms of exploration cost and that W-CPE-BAI becomes better when the level of personalization α is smaller than 0.5. |
| Researcher Affiliation | Collaboration | Clémence Réda Université Paris Cité, Inserm, Neuro Diderot, F-75019 Paris, France clemence.reda@inria.fr Sattar Vakili Media Tek Research, Cambourne Business Park, CB23 6DW, United Kingdom sattar.vakili@mtkresearch.com Emilie Kaufmann Université de Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRISt AL, F-59000 Lille, France emilie.kaufmann@univ-lille.fr |
| Pseudocode | Yes | Algorithm 1 Weighted Collaborative Phased Elimination for Best Arm Identification (W-CPE-BAI) |
| Open Source Code | Yes | Our code and run traces are available in an open-source repository. 5https://github.com/clreda/near-optimal-federated |
| Open Datasets | No | The paper states: 'We evaluated W-CPE-BAI against PF-UCB-BAI on a synthetic instance. We generated M = 10 agents and K = 10 arms, with fixed means (µk,m)k,m .' However, it does not provide concrete access information (link, DOI, citation) to this synthetic instance or the method to reproduce it. |
| Dataset Splits | No | The paper mentions generating a 'synthetic instance' and averaging results over '100 random seeds', but it does not describe explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | All the numerical experiments were performed on a personal computer (Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz x 4, 8GB RAM) using Python 3.8.10. |
| Software Dependencies | Yes | All the numerical experiments were performed on a personal computer (Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz x 4, 8GB RAM) using Python 3.8.10. The solvers used for the optimization problems are CVXPY 1.2.0 [9] and MOSEK 9.3.13 [27] (with SciPy 1.7.3 [33] as linear algebra backend). |
| Experiment Setup | No | The paper specifies the generation of the synthetic instance (e.g., M=10 agents, K=10 arms, α=0.5, 100 random seeds, fixed random seed for mean matrix generation). However, it does not provide specific hyperparameters like learning rates, batch sizes, or other training configuration details for the W-CPE-BAI or PF-UCB-BAI algorithms themselves. |