Principled Approaches for Learning to Defer to Multiple Experts

Kernkonzepte

The core message of this paper is to introduce a new family of surrogate losses specifically tailored for the multiple-expert setting, where the prediction and deferral functions are learned simultaneously. The authors prove that these surrogate losses benefit from strong H-consistency bounds, which are more relevant and advantageous than Bayes-consistency.

Zusammenfassung

The paper presents a study of surrogate losses and algorithms for the general problem of learning to defer with multiple experts. The key highlights are:

Introduction of a new family of surrogate losses specifically designed for the multiple-expert setting, where the prediction and deferral functions are learned simultaneously.
Proof that these surrogate losses benefit from strong H-consistency bounds, which are more relevant and advantageous than Bayes-consistency.
Illustration of the application of the analysis through several examples of practical surrogate losses, for which explicit guarantees are provided.
The H-consistency bounds incorporate the minimizability gap, which can lead to more favorable guarantees than bounds based on the approximation error.
Derivation of learning bounds for the deferral loss based on the H-consistency bounds and Rademacher complexity.
Experimental results on SVHN and CIFAR-10 datasets, demonstrating the positive correlation between the number of experts and the overall system accuracy.

Statistiken

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Zitate

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Wichtige Erkenntnisse aus

Principled Approaches for Learning to Defer with Multiple Experts

by Anqi Mao,Meh... um arxiv.org 04-02-2024

https://arxiv.org/pdf/2310.14774.pdf

Tiefere Fragen

What are some potential real-world applications where learning to defer with multiple experts could be particularly beneficial

Learning to defer with multiple experts can be highly beneficial in various real-world applications where expert decisions can complement or enhance existing models. Some potential applications include:

Medical Diagnosis: In healthcare, multiple experts, such as doctors and AI systems, can collaborate to provide accurate diagnoses. Deferring to specialists or advanced diagnostic tools can improve the accuracy of medical decisions.

Financial Trading: In the financial sector, deferring to different trading algorithms or expert analysts can help in making more informed investment decisions, especially in volatile markets.

Natural Language Processing: In language models and text generation systems, deferring to domain-specific experts or models can enhance the quality and accuracy of generated content.

Image Recognition: In image classification tasks, deferring to specialized models for specific classes or features can improve the overall accuracy of the classification system.

Autonomous Vehicles: In self-driving cars, deferring to different sensors or expert systems for decision-making can enhance safety and reliability on the road.

How can the proposed surrogate losses and H-consistency bounds be extended to other learning settings, such as regression or structured prediction tasks

The proposed surrogate losses and H-consistency bounds can be extended to other learning settings by adapting the framework to suit the specific requirements of tasks like regression or structured prediction. Here are some ways to extend them:

Regression Tasks: For regression tasks, the surrogate losses can be modified to handle continuous output spaces. The H-consistency bounds can be redefined to ensure the consistency of regression models with multiple experts.

Structured Prediction: In structured prediction tasks like sequence labeling or parsing, the surrogate losses can be tailored to handle complex output structures. The H-consistency bounds can be adapted to account for the structured nature of the predictions.

Transfer Learning: The framework can be extended to incorporate transfer learning scenarios where knowledge from multiple experts is leveraged to improve performance on new tasks.

Online Learning: The surrogate losses and consistency bounds can be applied to online learning settings where data arrives sequentially, ensuring the robustness and adaptability of the learning algorithm.

What are the implications of the minimizability gap on the design and optimization of the surrogate losses, and how can this be further explored to improve the practical performance of the learning to defer framework

The minimizability gap plays a crucial role in the design and optimization of surrogate losses for learning to defer with multiple experts. Here are some implications and ways to explore its impact further:

Regularization: The minimizability gap can act as a form of regularization, controlling the complexity of the hypothesis set and preventing overfitting. By understanding and leveraging this gap, the surrogate losses can be optimized more effectively.

Model Selection: The minimizability gap can guide the selection of the most suitable surrogate loss function based on the complexity of the task and the available experts. It can help in choosing the right balance between accuracy and deferral costs.

Algorithm Performance: Exploring the minimizability gap further can lead to the development of more robust learning algorithms that are capable of handling diverse expert configurations and cost functions. By studying its implications on different tasks, the practical performance of the learning to defer framework can be improved.