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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Approximations of the Restless Bandit Problem
Authors: Steffen Grünewälder, Azadeh Khaleghi
JMLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Proposed algorithms are accompanied with corresponding regret analysis. We provide an upper bound on the regret of Algorithm 1 (with respect to the highest stationary mean). This is provided in Theorem 12. We provide a simple algorithm, namely, Algorithm 2, that exploits the dependence between the pay-offs. |
| Researcher Affiliation | Academia | Steffen Grünewälder EMAIL Department of Mathematics and Statistics Lancaster University Lancaster, UK Azadeh Khaleghi EMAIL Department of Mathematics and Statistics Lancaster University Lancaster, UK |
| Pseudocode | Yes | Algorithm 1 A UCB-type Algorithm for ϕ-mixing bandits. Algorithm 2 An Algorithm for highly dependent arms. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code for the described methodology, nor does it provide a link to a code repository. The license mentioned (CC-BY 4.0) applies to the paper itself, not the implementation code. |
| Open Datasets | No | The paper focuses on theoretical analysis and algorithm design for the restless bandit problem, and does not mention the use of any specific public or open datasets for empirical evaluation or experimentation. |
| Dataset Splits | No | The paper is a theoretical work on the restless bandit problem, proposing algorithms and providing regret analysis. It does not include empirical experiments that would require dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and mathematical analysis. It does not describe any experimental setup or specific hardware used for computations. |
| Software Dependencies | No | The paper presents theoretical algorithms and mathematical proofs for the restless bandit problem. It does not specify any software dependencies with version numbers, as it does not describe an implementation or empirical evaluation. |
| Experiment Setup | No | The paper is theoretical, presenting algorithms and mathematical proofs. It does not provide specific experimental setup details, hyperparameter values, or training configurations for any empirical evaluation. |