Efficient PAC Learning from the Crowd with Pairwise Comparisons
Authors: Shiwei Zeng, Jie Shen
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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 <szeng4@stevens.edu>, Jie Shen <jie.shen@stevens.edu>. |
| 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. |