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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints
Authors: Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Federico Fusco
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 28,654 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,710 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper is theoretical and we do not have any experimental results. |
| Researcher Affiliation | Academia | Bocconi university Politecnico di Milano Sapienza University of Rome EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 and Algorithm 2 are present on page 2 and 3 respectively, providing structured pseudocode. |
| Open Source Code | No | The paper is theoretical and we do not have any experimental results. |
| Open Datasets | No | The paper is theoretical and we do not have any experimental results. |
| Dataset Splits | No | The paper is theoretical and we do not have any experimental results. |
| Hardware Specification | No | The paper is theoretical and we do not have any experimental results. |
| Software Dependencies | No | The paper is theoretical and we do not have any experimental results. |
| Experiment Setup | No | The paper is theoretical and we do not have any experimental results. |