As machine learning models are increasingly employed to assist human
decision-makers, it becomes critical to communicate the uncertainty associated
with these model predictions. However, the majority of work on uncertainty has
focused on traditional probabilistic or ranking approaches - where the model
assigns low probabilities or scores to uncertain examples. While this captures
what examples are challenging for the model, it does not capture the underlying
source of the uncertainty. In this work, we seek to identify examples the model
is uncertain about and characterize the source of said uncertainty. We explore
the benefits of designing a targeted intervention - targeted data augmentation
of the examples where the model is uncertain over the course of training. We
investigate whether the rate of learning in the presence of additional
information differs between atypical and noisy examples? Our results show that
this is indeed the case, suggesting that well-designed interventions over the
course of training can be an effective way to characterize and distinguish
between different sources of uncertainty.

研究机器学习模型中的不确定性，提出有针对性的数据增强方法以设计出对应不同不确定性来源的干预，结果表明，噪声数据和典型数据的学习率在引入额外信息后存在显著差异。

两只长尾的故事

A Tale Of Two Long Tails

Many learning tasks in machine learning can be viewed as taking a gradient
step towards minimizing the average loss of a batch of examples in each
training iteration. When noise is prevalent in the data, this uniform treatment
of examples can lead to overfitting to noisy examples with larger loss values
and result in poor generalization. Inspired by the expert setting in on-line
learning, we present a flexible approach to learning from noisy examples.
Specifically, we treat each training example as an expert and maintain a
distribution over all examples. We alternate between updating the parameters of
the model using gradient descent and updating the example weights using the
exponentiated gradient update. Unlike other related methods, our approach
handles a general class of loss functions and can be applied to a wide range of
noise types and applications. We show the efficacy of our approach for multiple
learning settings, namely noisy principal component analysis and a variety of
noisy classification problems.

研究了在机器学习中，每个训练周期都是将梯度步骤视为朝着最小化每个批次的例子的平均损失的方向，其中噪声会导致过度拟合到损失值较大的噪声样本，提出了一种使用指数梯度更新的加权学习方法，适用于一系列噪声类型和应用场景的损失函数。