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].
A PAC Framework for Aggregating Agents’ Judgments
Authors: Hanrui Zhang, Vincent Conitzer2237-2244
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a formal learning-theoretic framework for this setting. We then give general results on how to translate classical results from PAC learning into results in our framework. Subsequently, we show that in some settings, better results can be obtained by working directly in our framework. |
| Researcher Affiliation | Academia | Hanrui Zhang Computer Science Department Duke University Durham, NC 27705 EMAIL Vincent Conitzer Computer Science Department Duke University Durham, NC 27705 EMAIL |
| Pseudocode | Yes | ALGORITHM 1: Aggregation algorithm for binary judgments. ALGORITHM 2: Aggregation algorithm for discrete judgments. |
| Open Source Code | No | The paper does not provide any information about the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper introduces a theoretical framework and does not mention the use of specific, publicly available datasets for training or experimentation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation with dataset splits (training, validation, test). |
| Hardware Specification | No | The paper focuses on theoretical work and does not describe any computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper outlines a theoretical framework and algorithms but does not specify any software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |