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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |