Summary of Optimal Level Set Estimation For Non-parametric Tournament and Crowdsourcing Problems, by Maximilian Graf et al.
Optimal level set estimation for non-parametric tournament and crowdsourcing problems
by Maximilian Graf, Alexandra Carpentier, Nicolas Verzelen
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In a crowdsourcing setting where experts partially observe the correctness of their answers, researchers aim to decipher small entries of a probability matrix from larger ones. Assuming both experts and questions can be ordered, the authors focus on recovering level sets of the matrix with high precision, using the number of misclassified entries as a loss measure. A polynomial-time algorithm is constructed, which turns out to be minimax optimal for this classification problem. This contrasts with existing literature in strongly stochastic transitive models, where statistical-computational gaps were conjectured. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a world where many people are asked to answer questions online, researchers want to figure out how accurate each person’s answers are. They use math to try and understand patterns in the data, and come up with a new way to do this that is really efficient. This helps us understand how to get more accurate answers by assigning the right tasks to the right people. |
Keywords
» Artificial intelligence » Classification » Precision » Probability
