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..
Perfect Sampling from Pairwise Comparisons
Authors: Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work provided a theoretical understanding to perfect sampling from pairwise comparisons. We believe it is an interesting direction to experimentally evaluate our proposed methodology. |
| Researcher Affiliation | Academia | Dimitris Fotakis NTUA EMAIL Alkis Kalavasis NTUA EMAIL Christos Tzamos UW Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 Exact Sampling from Local Sampling Schemes of Informal Theorem 1 & Theorem 2 |
| Open Source Code | No | The paper explicitly states: 'This work is purely theoretical and has no negative societal impact.' It does not mention any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use empirical datasets. It refers to a 'Local Sampling Scheme Samp(Q; D)' as a theoretical sample oracle for its model, not an empirical dataset for training. |
| Dataset Splits | No | The paper is purely theoretical and does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |