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..
Efficient PAC Learning from the Crowd with Pairwise Comparisons
Authors: Shiwei Zeng, Jie Shen
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main algorithmic contributions are a comparisonequipped labeling scheme that can faithfully recover the true labels of a small set of instances, and a label-efficient filtering process that in conjunction with the small labeled set can reliably infer the true labels of a large instance set. The paper presents theoretical algorithms (e.g., Algorithm 1-4), mathematical proofs (theorems, lemmas, propositions), and complexity analysis, with no empirical evaluation on datasets reported for their own work. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA. Correspondence to: Shiwei Zeng <EMAIL>, Jie Shen <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 COMPARE-AND-LABEL; Algorithm 2 Main Algorithm; Algorithm 3 ANTI-ANTI-CONCENTRATE; Algorithm 4 FILTER. |
| Open Source Code | No | The paper does not contain any statement about releasing its source code or provide links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with specific datasets; therefore, it does not provide information about publicly available training data. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers that would be required to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and their theoretical properties, but it does not provide specific experimental setup details like hyperparameters or training configurations. |