Notice: 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. Full accuracy metrics and methodology are described in [1].
Linear Bandits on Uniformly Convex Sets
Authors: Thomas Kerdreux, Christophe Roux, Alexandre d'Aspremont, Sebastian Pokutta
JMLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here, we derive bandit algorithms for some strongly convex sets beyond ℓp balls that enjoy pseudo-regret bounds of O(n T). This result provides new elements for the open question in (Bubeck and Cesa-Bianchi, 2012, 5.5.). When the action set is q-uniformly convex but not necessarily strongly convex (q > 2), we obtain pseudo-regret bounds O(n1/q T 1/p) with p s.t. 1/p + 1/q = 1. These pseudo-regret bounds are competitive with the general O(n T) for a time horizon range that depends on the degree q > 2 of the set s uniform convexity and the dimension n of the problem. |
| Researcher Affiliation | Academia | Thomas Kerdreux EMAIL Zuse Institute Technische Universit at Berlin, Germany Christophe Roux EMAIL Zuse Institute Technische Universit at Berlin, Germany Alexandre d Aspremont EMAIL D epartement d Informatique Ecole Normale Sup erieure CNRS, UMR 8548 Paris, France Sebastian Pokutta EMAIL Zuse Institute Technische Universit at Berlin, Germany |
| Pseudocode | Yes | Algorithm 1: Bandit Mirror Descent (BMD) on some Curved Sets K Rn |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository in the main text or supplementary materials. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical derivations and proofs of pseudo-regret bounds for linear bandit algorithms. It does not involve experimental evaluation on specific datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments performed on datasets, thus no information on dataset splits is provided. |
| Hardware Specification | No | This paper presents theoretical results and algorithms. It does not include experimental evaluation, and therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical derivations and algorithms and does not describe experimental implementations that would require specific software dependencies or versions. |
| Experiment Setup | No | As a theoretical paper, it does not include experimental results or a description of an experimental setup with hyperparameters. |