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].
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
| 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. |