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..
Gamification of Pure Exploration for Linear Bandits
Authors: Rémy Degenne, Pierre Menard, Xuedong Shang, Michal Valko
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments Besides our algorithms, we implement the following algorithms... Fig. 1 shows the empirical stopping time of each algorithms averaged over 100 runs... Fig. 2. Sample complexity over random unit sphere vectors... |
| Researcher Affiliation | Collaboration | 1INRIA DIENS PSL Research University, Paris, France 2INRIA 3INRIA Universit e de Lille 4Deep Mind Paris. |
| Pseudocode | Yes | Algorithm 1 Lin Game Algorithm 2 Lin Game-C |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it include a specific repository link or an explicit code release statement. |
| Open Datasets | No | The paper evaluates on 'synthetic problem instances' and 'random unit sphere vectors', which are generated for the experiments. It does not provide access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | We set d = 2, α = π/6. Fig. 1 shows the empirical stopping time of each algorithms averaged over 100 runs, with a confidence level δ = 0.1, 0.01, 0.0001 from left to right... We set d = 6, 8, 10, 12 respectively, and always keep a same δ = 0.01. |