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].
Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints
Authors: Guanyu Nie, Vaneet Aggarwal, Christopher Quinn
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is primarily of theoretical nature and does not include experiments. |
| Researcher Affiliation | Academia | Guanyu Nie Iowa State University Ames, IA 50010 EMAIL Vaneet Aggarwal Purdue University West Lafayette, IN 47907 EMAIL Christopher John Quinn Iowa State University Ames, IA 50010 EMAIL |
| Pseudocode | Yes | Algorithm 1 OLSGA (Semi-bandit Feedback) and Algorithm 2 OLSGA with First Order Full Information are presented. |
| Open Source Code | No | Our paper is primarily of theoretical nature and does not include experiments. |
| Open Datasets | No | Our paper is primarily of theoretical nature and does not include experiments. |
| Dataset Splits | No | Our paper is primarily of theoretical nature and does not include experiments. |
| Hardware Specification | No | Our paper is primarily of theoretical nature and does not include experiments. |
| Software Dependencies | No | Our paper is primarily of theoretical nature and does not include experiments. |
| Experiment Setup | No | Our paper is primarily of theoretical nature and does not include experiments. |