Machine Learning and Deep Learning have achieved an impressive standard
today, enabling us to answer questions that were inconceivable a few years ago.
Besides these successes, it becomes clear, that beyond pure prediction, which
is the primary strength of most supervised machine learning algorithms, the
quantification of uncertainty is relevant and necessary as well. While first
concepts and ideas in this direction have emerged in recent years, this paper
adopts a conceptual perspective and examines possible sources of uncertainty.
By adopting the viewpoint of a statistician, we discuss the concepts of
aleatoric and epistemic uncertainty, which are more commonly associated with
machine learning. The paper aims to formalize the two types of uncertainty and
demonstrates that sources of uncertainty are miscellaneous and can not always
be decomposed into aleatoric and epistemic. Drawing parallels between
statistical concepts and uncertainty in machine learning, we also demonstrate
the role of data and their influence on uncertainty.

探讨机器学习和统计学中难以避免的不确定性问题，区分概率和信息性不确定性，并分析数据对这些不确定性的影响。

机器学习中的不确定性来源 -- 统计学家的视角

Sources of Uncertainty in Machine Learning -- A Statisticians' View

Due to the steadily increasing relevance of machine learning for practical
applications, many of which are coming with safety requirements, the notion of
uncertainty has received increasing attention in machine learning research in
the last couple of years. In particular, the idea of distinguishing between two
important types of uncertainty, often refereed to as aleatoric and epistemic,
has recently been studied in the setting of supervised learning. In this paper,
we propose to quantify these uncertainties with random forests. More
specifically, we show how two general approaches for measuring the learner's
aleatoric and epistemic uncertainty in a prediction can be instantiated with
decision trees and random forests as learning algorithms in a classification
setting. In this regard, we also compare random forests with deep neural
networks, which have been used for a similar purpose.

这篇论文介绍了机器学习中不确定性的概念及其研究应用。研究者通过随机森林及决策树来量化学习器的混淆不确定性及知识不确定性，并将其与深度神经网络进行了比较。

随机森林中的不确定性：演化不确定性和认知不确定性

Aleatoric and Epistemic Uncertainty with Random Forests

The notion of uncertainty is of major importance in machine learning and
constitutes a key element of machine learning methodology. In line with the
statistical tradition, uncertainty has long been perceived as almost synonymous
with standard probability and probabilistic predictions. Yet, due to the
steadily increasing relevance of machine learning for practical applications
and related issues such as safety requirements, new problems and challenges
have recently been identified by machine learning scholars, and these problems
may call for new methodological developments. In particular, this includes the
importance of distinguishing between (at least) two different types of
uncertainty, often referred to as aleatoric and epistemic. In this paper, we
provide an introduction to the topic of uncertainty in machine learning as well
as an overview of attempts so far at handling uncertainty in general and
formalizing this distinction in particular.

该研究论文评估了机器学习中不确定性的现状，并尝试区分 aleatoric 和 epistemic 两种不确定性的类型。

机器学习中的随机不确定性和认知不确定性：概念和方法介绍

Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction  to Concepts and Methods

Bayesian neural networks (BNNs) with latent variables are probabilistic
models which can automatically identify complex stochastic patterns in the
data. We describe and study in these models a decomposition of predictive
uncertainty into its epistemic and aleatoric components. First, we show how
such a decomposition arises naturally in a Bayesian active learning scenario by
following an information theoretic approach. Second, we use a similar
decomposition to develop a novel risk sensitive objective for safe
reinforcement learning (RL). This objective minimizes the effect of model bias
in environments whose stochastic dynamics are described by BNNs with latent
variables. Our experiments illustrate the usefulness of the resulting
decomposition in active learning and safe RL settings.

本文研究贝叶斯神经网络和潜在变量的概率模型，使用信息论方法和风险敏感目标分别将预测不确定性分解为认知和概率性成分，以解决主动学习和安全强化学习等领域中的模型偏差问题。