Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
Authors: Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I Jordan
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 {yuczhang,jordan}@berkeley.edu New York University, New York, NY 10012 xichen@nyu.edu Microsoft Research, 1 Microsoft Way, Redmond, WA 98052 dengyong.zhou@microsoft.com |
| 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 . |