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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Approximation Theory Based Methods for RKHS Bandits
Authors: Sho Takemori, Masahiro Sato
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In synthetic environments, we empirically show that APG-UCB has almost the same cumulative regret as that of IGP-UCB and its running time is much shorter. |
| Researcher Affiliation | Industry | 1 FUJIFILM Business Innovation, Kanagawa, Japan. |
| Pseudocode | Yes | Algorithm 1 Construction of Newton basis with P-greedy algorithm (c.f. Pazouki & Schaback (2011)) |
| Open Source Code | No | The paper does not explicitly state the release of source code or provide a link to a repository for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic environments and reward functions for experiments, but does not refer to a publicly available dataset with concrete access information (link, DOI, or specific citation to an established dataset). |
| Dataset Splits | No | The paper describes synthetic environments and online learning simulations, but does not specify training/validation/test dataset splits for reproducibility. |
| Hardware Specification | Yes | Computation is done by Intel Xeon E5-2630 v4 processor with 128 GB RAM. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | We take l = 0.3 for the RQ kernel and l = 0.2 for the SE kernel, because the diameter of the d-dimensional cube is d. For each kernel, we generate 10 reward functions as above and evaluate our proposed method and the existing algorithm for time interval T = 5000 in terms of mean cumulative regret and total running time. |