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A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.


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.

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.