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..
Pareto Regret Analyses in Multi-objective Multi-armed Bandit
Authors: Mengfan Xu, Diego Klabjan
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The algorithms are shown optimal in adversarial settings and nearly optimal up to a logarithmic factor in stochastic settings simultaneously by our established upper bounds and lower bounds on Pareto regrets. |
| Researcher Affiliation | Academia | 1Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL 60208, U.S.A.. Correspondence to: Mengfan Xu <Mengfan EMAIL>, Diego Klabjan <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Algorithm With Known s (MO-KS) and Algorithm 2 Algorithm With Unknown s and T (MO-US) |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no train/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |