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].
Epsilon Best Arm Identification in Spectral Bandits
Authors: Tomáš Kocák, Aurélien Garivier
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For the experiments, we used bandit problem µ = (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0.1, 0) with K = 11 arms... The following plot demonstrates the effect of smoothness parameter R on both theoretical and empirical stopping times. The green curve represents the average stopping time of 10 runs of Spectral Ta S while the red curve represents the characteristic time. |
| Researcher Affiliation | Academia | Tom aˇs Koc ak and Aur elien Garivier Unit e de Math ematiques Pures et Appliqu ees et Laboratoire de l Informatique du Parall elisme Ecole Normale Sup erieure de Lyon, Universit e de Lyon EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Spectral Ta S |
| Open Source Code | No | No statement about releasing code or links to a code repository are provided. |
| Open Datasets | No | The paper defines a synthetic bandit problem µ = (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0.1, 0) with K=11 arms for its experiments, which is generated for the paper rather than being a pre-existing public dataset with specific access information. |
| Dataset Splits | No | The paper describes a simulation setup for a bandit problem and averages results over "10 runs" but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software names with version numbers. |
| Experiment Setup | Yes | For the experiments, we used bandit problem µ = (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0.1, 0) with K = 11 arms... ε = 0.05, and different values of R. Algorithm 1 Spectral Ta S Input and initialization: L : graph Laplacian ε, δ : tolerance and confidence parameters R : upper bound on the smoothness of µ |