Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Non-Asymptotic Pure Exploration by Solving Games
Authors: Rémy Degenne, Wouter M. Koolen, Pierre Ménard
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,443 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,426 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach empirically in benchmark experiments at practical δ, and find that our algorithms are either competitive with Track-and-Stop (dense w ) or dominate it (sparse w ). |
| Researcher Affiliation | Academia | Rémy Degenne Centrum Wiskunde & Informatica Science Park 123, 1098 XG Amsterdam EMAIL |
| Pseudocode | Yes | Algorithm 1 Pure exploration meta-algorithm. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes experiments on 'Bernoulli bandit model' and 'Gaussian bandit model' with specified parameters, indicating a simulated environment rather than the use of a pre-existing publicly available dataset that would require a link or citation for access. |
| Dataset Splits | No | The paper operates within a multi-armed bandit framework where data is sampled sequentially, and therefore, does not discuss traditional training, validation, and test dataset splits as found in static dataset-based experiments. |
| Hardware Specification | No | The paper states 'The experiments were carried out on the Dutch national e-infrastructure with the support of SURF Cooperative,' but it does not provide specific hardware details such as GPU/CPU models, memory, or processor types. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | We use stylised stopping threshold β(δ, t) = ln 1+ln t / δ and exploration bonus f(t) = ln t. Both are unlicensed by theory yet conservative in practise (the error frequency is way below δ). |