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..
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits
Authors: Siddharth Barman, Arindam Khan, Arnab Maiti, Ayush Sawarni
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work develops an algorithm that, given the horizon of play T, achieves a Nash regret of O q , here k denotes the number of arms in the MAB instance. ... We develop an algorithm that achieves Nash regret of ; here, k denotes the number of arms in the bandit instance and T is the given horizon of play (Theorem 1 and Theorem 2). |
| Researcher Affiliation | Academia | 1 Indian Institute of Science 2 University of Washington |
| Pseudocode | Yes | Algorithm 1: Nash Con๏ฌdence Bound Algorithm |
| Open Source Code | No | No statement providing concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper was found. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and proofs, not empirical evaluation on specific datasets. Therefore, no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with datasets; therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs, not empirical experimental setup details like hyperparameters or training configurations. |