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].
A Gang of Adversarial Bandits
Authors: Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present two learning algorithms, GABA-I and GABA-II which exploit the network structure to bias towards functions of low Ψ values. We show that GABA-I has an expected regret bound of O( p ln(NK/Ψ)ΨKT) and per-trial time complexity of O(K ln(N)), whilst GABA-II has a weaker O( p ln(N/Ψ) ln(NK/Ψ)ΨKT) regret, but a better O(ln(K) ln(N)) per-trial time complexity. |
| Researcher Affiliation | Academia | Mark Herbster*, Stephen Pasteris* Department of Computer Science University College London London WC1E 6BT EMAIL Fabio Vitale University of Lille 59653 Villeneuve d Ascq CEDEX France EMAIL Massimiliano Pontil CSML, Istituto Italiano di Tecnologia and Department of Computer Science University College London EMAIL |
| Pseudocode | Yes | Figure 1: Binary Support Tree Construction Algorithm, Figure 2: SPECIALISTEXP Algorithm, Figure 3: GABA-I Algorithm, Figure 4: GABA-II Algorithm |
| Open Source Code | No | The paper does not contain any explicit statements about releasing code or links to source code repositories for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention any specific dataset used for training or provide access information for any dataset. |
| Dataset Splits | No | The paper focuses on theoretical analysis and algorithm design, and thus does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical in nature, presenting algorithms and regret bounds, and therefore does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is a theoretical work and does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper presents theoretical algorithms and their analysis, without detailing an empirical experimental setup or specific hyperparameters. |