Finite Continuum-Armed Bandits
Authors: Solenne Gaucher
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 solenne.gaucher@math.u-psud.fr |
| 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. |