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..
On the Universal Near Optimality of Hedge in Combinatorial Settings
Authors: Zhiyuan Fan, Arnab Maiti, Lillian J. Ratliff, Kevin G. Jamieson, Gabriele Farina
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the classical HEDGE algorithm in combinatorial settings. ... We defer the proof to Appendix A. ... We defer the proof to Appendix B. ... We defer the proof to Appendix C. ... The full proof is deferred to Appendix D. ... Our paper is theoretical in nature. |
| Researcher Affiliation | Academia | Zhiyuan Fan MIT EMAIL; Arnab Maiti University of Washington EMAIL; Kevin Jamieson University of Washington EMAIL; Lillian J. Ratliff University of Washington EMAIL; Gabriele Farina MIT EMAIL |
| Pseudocode | No | The paper describes algorithms like HEDGE and OMD but does not present them in a structured pseudocode block. It describes their function mathematically and conceptually in text. |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: The paper does not include experiments. |
| Open Datasets | No | Question: Does the paper provide CONCRETE ACCESS INFORMATION (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset? Answer: [NA] Justification: The paper does not include experiments. |
| Dataset Splits | No | Question: Does the paper explicitly provide training/test/validation dataset splits needed to reproduce the experiment? Answer: [NA] Justification: The paper does not include experiments. |
| Hardware Specification | No | Question: Does the paper explicitly describe the hardware used to run its experiments? Answer: [NA] Justification: The paper does not include experiments. |
| Software Dependencies | No | Question: Does the paper provide a reproducible description of the ancillary software. A reproducible description must include specific version numbers for key software components. Answer: [NA] Justification: The paper does not include experiments. |
| Experiment Setup | No | Question: Does the paper explicitly provide details about the experimental setup, especially hyperparameters or system-level training settings? Answer: [NA] Justification: The paper does not include experiments. |