Collaborative Pure Exploration in Kernel Bandit
Authors: Yihan Du, Wei Chen, Yuko Kuroki, Longbo Huang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide the experimental results. Here we set V = 5, n = 6, δ = 0.005, H = Rd, d {4, 8, 20}, θ = [0.1, 0.1 + Δmin, . . . , 0.1 + (d 1)Δmin] , Δmin [0.1, 0.8] and rank(KZ) [1, V ]. We run 50 independent simulations and plot the average sample complexity with 95% confidence intervals (see Appendix A for a complete setup description and more results). |
| Researcher Affiliation | Collaboration | Yihan Du Institute for Interdisciplinary Information Sciences Tsinghua University Beijing, China duyh18@mails.tsinghua.edu.cn Wei Chen Microsoft Research Beijing, China weic@microsoft.com Yuko Kuroki The University of Tokyo & RIKEN AIP, Tokyo, Japan CENTAI Institute, Turin, Italy yuko.kuroki@centai.eu Longbo Huang Institute for Interdisciplinary Information Sciences Tsinghua University Beijing, China longbohuang@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 Collaborative Multi-agent Algorithm Co Kernel FC: for Agent v [V ] [...] Algorithm 2 Collaborative Multi-agent Algorithm Co Kernel FB: for Agent v [V ] |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code or a link to a code repository. |
| Open Datasets | No | The paper describes a synthetic experimental setup where parameters like V, n, δ, H, d, θ, Δmin, and rank(KZ) are set or tuned to generate instances for simulations, rather than using a pre-existing publicly available dataset. There is no mention of an external dataset being used that would require an access link or citation. |
| Dataset Splits | No | The paper describes generating different instances for simulations and running independent simulations (50 or 100 runs) to plot average metrics. This is not the same as providing specific train/validation/test dataset splits for reproducibility, as the data is generated within the simulation rather than partitioned from a fixed dataset. |
| Hardware Specification | Yes | Our experiments are run on Intel Xeon E5-2660 v3 CPU at 2.60GHz. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, specific libraries or solvers). It only describes the conceptual algorithms and experimental setup parameters. |
| Experiment Setup | Yes | Here we set V = 5, n = 6, δ = 0.005, H = Rd, d {4, 8, 20}, θ = [0.1, 0.1 + Δmin, . . . , 0.1 + (d 1)Δmin] , Δmin [0.1, 0.8] and rank(KZ) [1, V ]. We run 50 independent simulations and plot the average sample complexity with 95% confidence intervals (see Appendix A for a complete setup description and more results). [...] For the rank(KZ) = 1 case, we set d = 4. For any v [V ], {φ( x)} x Xv is the set of all 4 2 vectors in R4, where each vector has two entries 0 and two entries 1. |