Pareto Regret Analyses in Multi-objective Multi-armed Bandit
Authors: Mengfan Xu, Diego Klabjan
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 Xu2023@u.northwestern.edu>, Diego Klabjan <dklabjan@northwestern.edu>. |
| 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. |