Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data

Core Concepts

The core message of this paper is to develop a method for forming valid confident prediction sets in the out-of-distribution (OOD) generalization setting, where the test distribution differs from the training distribution.

Abstract

The paper studies the confidence set prediction problem in the OOD generalization setting. It first shows that the standard split conformal prediction (SCP) method fails to maintain the desired marginal coverage when the test distribution differs from the training distribution.
To address this issue, the paper proposes a new method for forming confident prediction sets in the OOD setting. The key idea is to construct the prediction set based on the f-divergence between the test distribution and the convex hull of the training distributions. The paper provides a theoretical analysis to show that the proposed method is guaranteed to maintain the marginal coverage for any target distribution within a certain f-divergence ball of the training distributions.
The paper also considers the practical case where only the empirical distributions of the training data are available. It provides a corrected version of the prediction set that still maintains the marginal coverage guarantee in this setting.
Finally, the paper conducts simulation experiments to verify the correctness of the theoretical results and the validity of the proposed method.

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Key Insights Distilled From

Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data

by Xin Zou,Weiw... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19950.pdf

Deeper Inquiries

How can the proposed method be extended to handle more general distributional shifts, such as those caused by adversarial attacks

To extend the proposed method to handle more general distributional shifts, such as those caused by adversarial attacks, we can incorporate robustness measures into the model. By introducing adversarial training techniques, like adversarial examples generation during the training phase, the model can learn to be more resilient to distributional shifts caused by adversarial attacks. Additionally, we can explore techniques like domain adaptation or domain generalization to make the model more robust to unseen distributions. By incorporating these strategies, the model can better handle adversarial attacks and more general distributional shifts.

How can the proposed method be adapted to other uncertainty quantification tasks beyond confidence set prediction, such as uncertainty-aware decision making

The proposed method can be adapted to other uncertainty quantification tasks beyond confidence set prediction by modifying the scoring function and the threshold selection process. For uncertainty-aware decision making, the model can be trained to output not only a confidence set but also additional information about the uncertainty associated with each prediction. This can be achieved by incorporating techniques like Bayesian inference, ensemble methods, or Monte Carlo dropout to capture the model's uncertainty. By providing a more comprehensive understanding of uncertainty, the model can support decision-making processes that require a nuanced understanding of the prediction's reliability.

What are the potential applications of the proposed method in high-stakes domains like medical diagnosis or autonomous driving, where reliable uncertainty quantification is crucial

The proposed method has significant potential applications in high-stakes domains like medical diagnosis or autonomous driving, where reliable uncertainty quantification is crucial. In medical diagnosis, the model can provide not only predictions but also confidence intervals or prediction sets, allowing healthcare professionals to make informed decisions based on the model's uncertainty. This can help in scenarios where false positives or false negatives can have serious consequences. In autonomous driving, the model can provide uncertainty estimates about its predictions, enabling the vehicle to make safer decisions in uncertain or unfamiliar situations. By incorporating uncertainty quantification, the model can enhance safety and reliability in critical domains.