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..
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
Authors: Aadirupa Saha, Aditya Gopalan
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical performance results are also reported. |
| Researcher Affiliation | Academia | 1Indian Institute of Science, Bangalore, India. Correspondence to: Aadirupa Saha <EMAIL>. |
| Pseudocode | Yes | The complete algorithm is given in Appendix A.1. ... The pseudocode is moved to Appendix C.2. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | No | The paper mentions running experiments on 'different datasets' but does not provide specific access information (e.g., links, DOIs, or citations with author/year for public datasets). |
| Dataset Splits | No | The paper mentions numerical experiments but does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The default values of the parameters are set to be k = 5, ϵ = 0.01, δ = 0.01, m = 1 unless explicitly mentioned/tuned in the specific experimental setup. |