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..
Minimal Exploration in Structured Stochastic Bandits
Authors: Richard Combes, Stefan Magureanu, Alexandre Proutiere
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the efficiency of OSSB using numerical experiments in the case of the linear bandit problem and show that OSSB outperforms existing algorithms, including Thompson sampling. |
| Researcher Affiliation | Academia | Richard Combes Centrale-Supelec / L2S EMAIL Stefan Magureanu KTH, EE School / ACL EMAIL Alexandre Proutiere KTH, EE School / ACL EMAIL |
| Pseudocode | Yes | Algorithm 1 OSSB(ε,γ) |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes a synthetic experimental setup where parameters were generated uniformly at random, rather than using a pre-existing, publicly available dataset with concrete access information (e.g., URL, DOI, specific citation to an established benchmark). |
| Dataset Splits | No | The paper describes numerical experiments using synthetically generated parameters and mentions averaging over multiple trials, but it does not specify explicit training, validation, or test dataset splits, or cross-validation methods. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions baselines (e.g., Thompson Sampling, GLM-UCB) but does not provide specific version numbers for any software, libraries, or dependencies used in the experiments. |
| Experiment Setup | Yes | In our implementation of OSSB, we use γ = ε = 0 since γ is typically chosen 0 in the literature (see [18]) and the performance of the algorithm does not appear sensitive to the choice of ε. As baselines we select the extension of Thompson Sampling presented in [4](using vt = R p 0.5dln(t/δ), we chose δ = 0.1, R = 1), GLM-UCB (using ρ(t) = p 0.5ln(t)), an extension of UCB [16] and the algorithm presented in [31]. |