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.
Pure Exploration with Multiple Correct Answers
Authors: Rémy Degenne, Wouter M. Koolen
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,688 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,674 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We determine the sample complexity of pure exploration bandit problems with multiple good answers. We derive a lower bound using a new game equilibrium argument. We show how continuity and convexity properties of single-answer problems ensure that the existing Track-and-Stop algorithm has asymptotically optimal sample complexity. However, that convexity is lost when going to the multiple-answer setting. We present a new algorithm which extends Track-and Stop to the multiple-answer case and has asymptotic sample complexity matching the lower bound. |
| Researcher Affiliation | Academia | Rémy Degenne Centrum Wiskunde & Informatica Science Park 123, Amsterdam, NL EMAIL Wouter M. Koolen Centrum Wiskunde & Informatica Science Park 123, Amsterdam, NL EMAIL |
| Pseudocode | Yes | Algorithm 1 Sticky Track-and-Stop. Input: δ > 0, strict total order on I. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention specific datasets or their public availability for training. |
| Dataset Splits | No | The paper is theoretical and does not mention dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical algorithm design and analysis, and does not provide specific experimental setup details like hyperparameter values or training configurations. |