Summary of A Two-stage Learning-to-defer Approach For Multi-task Learning, by Yannis Montreuil et al.
A Two-Stage Learning-to-Defer Approach for Multi-Task Learning
by Yannis Montreuil, Shu Heng Yeo, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 The Two-Stage Learning-to-Defer framework is a popular approach for classification and regression tasks. However, many real-world applications involve both tasks in an interdependent manner. This paper introduces a novel Two-Stage Learning-to-Defer framework that jointly addresses these tasks. The approach leverages a two-stage surrogate loss family, which provides strong theoretical guarantees of convergence to the Bayes-optimal rejector. Theoretical consistency bounds are established, explicitly linked to the cross-entropy surrogate family and the L_1-norm of the agents’ costs. The framework is validated on challenging tasks such as object detection and electronic health record analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to learn about both classification and regression tasks together. Most current methods only work well for one task or the other, but this approach can handle both at once. This is important because many real-world problems involve both types of tasks. The method uses a special kind of loss function that helps it make good decisions. The researchers tested their approach on two difficult tasks and showed that it works better than current methods. |
Keywords
* Artificial intelligence * Classification * Cross entropy * Loss function * Object detection * Regression
