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.
Finite Continuum-Armed Bandits
Authors: Solenne Gaucher
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,797 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,367 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 propose an optimal strategy for this problem. Under natural assumptions on the reward function, we prove that the optimal regret scales as O(T 1/3) up to poly-logarithmic factors when the budget T is proportional to the number of actions N. When T becomes small compared to N, a smooth transition occurs. When the ratio T/N decreases from a constant to N 1/3, the regret increases progressively up to the O(T 1/2) rate encountered in continuum-armed bandits. |
| Researcher Affiliation | Academia | Solenne Gaucher Laboratoire de Mathématiques d Orsay Université Paris-Saclay, 91405, Orsay, France EMAIL |
| Pseudocode | Yes | Algorithm 1 Upper Confidence Bound for Finite continuum-armed bandits (UCBF) |
| Open Source Code | No | Not found. The paper is theoretical and describes an algorithm but does not mention providing access to its source code. |
| Open Datasets | No | Not found. The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | Not found. The paper is theoretical and does not involve experimental validation on datasets, thus no dataset splits are mentioned. |
| Hardware Specification | No | Not found. The paper is purely theoretical and does not describe any computational experiments that would require hardware specifications. |
| Software Dependencies | No | Not found. The paper is purely theoretical and does not describe any computational experiments that would require software dependencies with version numbers. |
| Experiment Setup | No | Not found. The paper is purely theoretical and does not describe any experimental setup or hyperparameters. |