In machine learning, accurately predicting the probability that a specific
input is correct is crucial for risk management. This process, known as
uncertainty (or confidence) estimation, is particularly important in
mission-critical applications such as autonomous driving. In this work, we put
forward a novel geometric-based approach for improving uncertainty estimations
in machine learning models. Our approach involves using the geometric distance
of the current input from existing training inputs as a signal for estimating
uncertainty, and then calibrating this signal using standard post-hoc
techniques. We demonstrate that our method leads to more accurate uncertainty
estimations than recently proposed approaches through extensive evaluation on a
variety of datasets and models. Additionally, we optimize our approach so that
it can be implemented on large datasets in near real-time applications, making
it suitable for time-sensitive scenarios.

本文提出了一种基于几何的方法来改善机器学习模型中的不确定性（或置信度）估计，结果表明相比于最近提出的方法更准确，并且可优化其实现以适用于需实时响应的大数据集应用的关键词是 “机器学习”、“不确定性估计”、“风险管理”、“基于几何的方法” 和 “事后技术”。

基于几何分离的不确定性估计

Uncertainty Estimation based on Geometric Separation

Machine-learning models have demonstrated great success in learning complex
patterns that enable them to make predictions about unobserved data. In
addition to using models for prediction, the ability to interpret what a model
has learned is receiving an increasing amount of attention. However, this
increased focus has led to considerable confusion about the notion of
interpretability. In particular, it is unclear how the wide array of proposed
interpretation methods are related, and what common concepts can be used to
evaluate them.
We aim to address these concerns by defining interpretability in the context
of machine learning and introducing the Predictive, Descriptive, Relevant (PDR)
framework for discussing interpretations. The PDR framework provides three
overarching desiderata for evaluation: predictive accuracy, descriptive
accuracy and relevancy, with relevancy judged relative to a human audience.
Moreover, to help manage the deluge of interpretation methods, we introduce a
categorization of existing techniques into model-based and post-hoc categories,
with sub-groups including sparsity, modularity and simulatability. To
demonstrate how practitioners can use the PDR framework to evaluate and
understand interpretations, we provide numerous real-world examples. These
examples highlight the often under-appreciated role played by human audiences
in discussions of interpretability. Finally, based on our framework, we discuss
limitations of existing methods and directions for future work. We hope that
this work will provide a common vocabulary that will make it easier for both
practitioners and researchers to discuss and choose from the full range of
interpretation methods.

该研究呈现了机器学习的可解释性与 PDR 框架，探讨了现有解释方法的分类与优劣评估，提供了实际案例并讨论了未来研究方向。