Near Optimal Best Arm Identification for Clustered Bandits
Authors: Yash, Avishek Ghosh, Nikhil Karamchandani
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world (Movie Lens, Yelp) data demonstrates the superior performance of the proposed algorithms in terms of sample and communication efficiency, particularly in settings where M N. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, IIT Bombay, India 2Department of Computer Science and Engineering, IIT Bombay, India. Correspondence to: Avishek Ghosh <avishek EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Cl-BAI Algorithm 2 SE (A,γ,R) Algorithm 3 BAI-Cl Algorithm 4 d SE(S,µS,γ,η,η1) |
| Open Source Code | No | The paper describes algorithms and experiments but does not provide any explicit statement about releasing source code, nor does it include a link to a code repository. |
| Open Datasets | Yes | Experiments on synthetic and real-world (Movie Lens, Yelp) data... We perform experiments using the Movie Lens-1M dataset... We perform experiments using the Yelp6 dataset, which contains ratings for various businesses given by users across different states of the US. 6https://www.yelp.com/dataset |
| Dataset Splits | No | For the three datasets constructed above, we vary N and plot the average number of pulls for the various schemes in Figures 1(a)(b)(c)... Finally, there are N agents, divided into N/M sized clusters... We group the users into six age categories: 18 24, 25 34, 35 44, 45 49, 50 55, and 56+... As before, we assume that there are N agents divided into M equal-sized clusters. The paper describes the creation of clusters and varying parameters like N, but does not specify explicit train/test/validation dataset splits typically used for model evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper describes the algorithms (e.g., Successive Elimination) but does not list any specific software dependencies or libraries with version numbers (e.g., Python, PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | We set the error probability δ =10^-10 for our experiments and present sample complexity results which are averaged over multiple independent runs of the corresponding algorithms... Input: δ, η; Initialize: Best Arm 0N ... γ =( δ 12NK )^4/3,R=log(17/η)... γ =δ/(2M)... Input: η,δ... γ = δ.log( M M 1 ) δ ) ,R=log(1/η)+1)... γ =δ/3N... S,µS,γ,η,η1... δk =10^-k... |