Summary of Regression with Multi-expert Deferral, by Anqi Mao et al.
Regression with Multi-Expert Deferral
by Anqi Mao, Mehryar Mohri, Yutao Zhong
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 this paper, researchers propose a novel framework for regression problems where the learner can choose to defer predictions to multiple experts. This approach presents unique challenges in regression due to the infinite and continuous nature of the label space. The authors introduce surrogate loss functions for both single-stage and two-stage scenarios, which provide stronger consistency guarantees than Bayes consistency. The proposed framework is versatile, accommodating various expert numbers, cost types, and loss functions. Experimental results demonstrate the effectiveness of the proposed algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new method to help machines learn from multiple experts when the answer is a number (regression problem). This is different from classification problems where the answer is a category. The approach allows machines to choose which expert to follow, and it provides stronger guarantees that the machine will make accurate predictions. |
Keywords
» Artificial intelligence » Classification » Regression
