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..
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
Authors: Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I Jordan
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on synthetic and real datasets. Experimental results demonstrate that the proposed algorithm is comparable to the most accurate empirical approach, while outperforming several other recently proposed methods. |
| Researcher Affiliation | Collaboration | University of California, Berkeley, Berkeley, CA 94720 EMAIL New York University, New York, NY 10012 EMAIL Microsoft Research, 1 Microsoft Way, Redmond, WA 98052 EMAIL |
| Pseudocode | Yes | Algorithm 1: Estimating confusion matrices |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | The datasets used (Bird, RTE, TREC, Dog, Web) are listed in Table 1 and cited (e.g., [22] for Bird, [21] for RTE, [16] for TREC, [9] for Dog, [26] for Web), indicating they are established, publicly accessible datasets. |
| Dataset Splits | No | The paper does not explicitly state training, validation, and test splits with percentages or sample counts. It refers to 'real datasets' on which evaluation is performed. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The default choice of the thresholding parameter is = 10 6. For components that are smaller than , they are reset to . |