Federated $\mathcal{X}$-armed Bandit with Flexible Personalisation
Authors: Ali Arabzadeh, James A. Grant, David S. Leslie
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical analysis and empirical evaluations demonstrate the effectiveness of our approach compared to existing methods. Potential applications of this work span various domains, including healthcare, smart home devices, and e-commerce, where balancing personalisation with global insights is crucial. ... In this section we evaluate the empirical performance of PF-XAB through a series of experiments employing synthetic objective functions and real-world dataset. |
| Researcher Affiliation | Academia | Ali Arabzadeh EMAIL School of Mathematical Sciences Lancaster University James A. Grant EMAIL School of Mathematical Sciences Lancaster University David S. Leslie EMAIL School of Mathematical Sciences Lancaster University |
| Pseudocode | Yes | Algorithm 1 Fed-XAB: server ... Algorithm 2 PF-XAB: m-th client |
| Open Source Code | No | No explicit statement or link to source code release for the methodology described in this paper was found. |
| Open Datasets | Yes | In addition to our initial experiments with synthetic objective functions, we extended our experiments to a real-world-inspired scenario using the landmine dataset (Liu et al., 2007). |
| Dataset Splits | No | The paper does not explicitly provide details about training/test/validation dataset splits. It mentions using the 'landmine dataset' and 'synthetic objective functions' but no specific split percentages or methodologies. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU models, memory amounts) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. It mentions using a 'Support Vector Machine (SVM) classifier equipped with RBF kernels' but no software versions. |
| Experiment Setup | Yes | Throughout this section, all experiments has been conducted on a federation with M = 10 clients. ... with M = 10 machines over T = 2 106 Time Steps. ... Our experiments explore d = 2 kernel parameters, γ ranging in [0.01, 10], and the regularisation parameter C, selected from the range [10 4, 104], forms the domain space of our experiments. |