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..
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. |