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].
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. |