To facilitate a wide-spread acceptance of AI systems guiding decision making
in real-world applications, trustworthiness of deployed models is key. That is,
it is crucial for predictive models to be uncertainty-aware and yield
well-calibrated (and thus trustworthy) predictions for both in-domain samples
as well as under domain shift. Recent efforts to account for predictive
uncertainty include post-processing steps for trained neural networks, Bayesian
neural networks as well as alternative non-Bayesian approaches such as ensemble
approaches and evidential deep learning. Here, we propose an efficient yet
general modelling approach for obtaining well-calibrated, trustworthy
probabilities for samples obtained after a domain shift. We introduce a new
training strategy combining an entropy-encouraging loss term with an
adversarial calibration loss term and demonstrate that this results in
well-calibrated and technically trustworthy predictions for a wide range of
domain drifts. We comprehensively evaluate previously proposed approaches on
different data modalities, a large range of data sets including sequence data,
network architectures and perturbation strategies. We observe that our
modelling approach substantially outperforms existing state-of-the-art
approaches, yielding well-calibrated predictions under domain drift.

为了让实际应用的人工智能系统更加被广泛认知，模型的可信度至关重要。文章提出了一种新的训练策略，结合熵鼓励损失项和对抗校准损失项，以提高样本在领域世界偏移下的校准性和适应性。文章在不同的数据模式、数据集和网络架构上进行了全面的评估，结果显示该方法明显高于现有的最先进方法，应用于领域漂移下预测方面具有技术上的可信度。

用快速对抗校准实现深度神经网络中值得信赖的预测

Towards Trustworthy Predictions from Deep Neural Networks with Fast  Adversarial Calibration

This paper continues study, both theoretical and empirical, of the method of
Venn prediction, concentrating on binary prediction problems. Venn predictors
produce probability-type predictions for the labels of test objects which are
guaranteed to be well calibrated under the standard assumption that the
observations are generated independently from the same distribution. We give a
simple formalization and proof of this property. We also introduce Venn-Abers
predictors, a new class of Venn predictors based on the idea of isotonic
regression, and report promising empirical results both for Venn-Abers
predictors and for their more computationally efficient simplified version.

本文研究了 Venn 预测法在二元预测问题上的应用，证明了其输出结果的良好校准性，并介绍了一种基于保序回归原理的 Venn-Abers 预测器，通过简化算法提高了计算效率并取得了良好的实验结果。