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].
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. |