Polynomial Cost of Adaptation for X-Armed Bandits
Authors: Hedi Hadiji
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we show that in the sequential setting of multi-armed bandits, the necessary exploration forces a similar phenomenon, and we exhibit this polynomial cost of adaptation. To do so, we revisit the lower bounds of Locatelli and Carpentier [21], and design a new algorithm that matches these lower bounds. |
| Researcher Affiliation | Academia | Hédi Hadiji Laboratoire de Mathématiques d Orsay Université Paris-Sud, Orsay, France hedi.hadiji@math.u-psud.fr |
| Pseudocode | Yes | Algorithm 1 CAB1.1 (Continuum-Armed Bandit, adapted from Kleinberg [16]) and Algorithm 2 Me DZO (Memorize, Discretize, Zoom Out) are provided. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | The paper is theoretical and analyzes algorithms based on mathematical models (e.g., 'reward function f: X -> [0, 1]'), not on external datasets requiring public access information. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation on datasets with training, validation, or test splits. |
| Hardware Specification | No | The paper is primarily theoretical, focusing on algorithm design and mathematical proofs. It does not mention any specific hardware used for computational experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the theoretical setup of the algorithms and their parameters (e.g., 'input parameter B') but does not provide specific experimental setup details like hyperparameters for running empirical simulations or experiments. |