Domain generalization aims to learn a model with good generalization ability,
that is, the learned model should not only perform well on several seen domains
but also on unseen domains with different data distributions. State-of-the-art
domain generalization methods typically train a representation function
followed by a classifier jointly to minimize both the classification risk and
the domain discrepancy. However, when it comes to model selection, most of
these methods rely on traditional validation routines that select models solely
based on the lowest classification risk on the validation set. In this paper,
we theoretically demonstrate a trade-off between minimizing classification risk
and mitigating domain discrepancy, i.e., it is impossible to achieve the
minimum of these two objectives simultaneously. Motivated by this theoretical
result, we propose a novel model selection method suggesting that the
validation process should account for both the classification risk and the
domain discrepancy. We validate the effectiveness of the proposed method by
numerical results on several domain generalization datasets.

本研究旨在解决域泛化问题，提出了一种新的模型选择方法，该方法综合考虑分类风险和域差异。通过实验结果验证了该方法的有效性。

领域泛化中模型验证的原则方法

A principled approach to model validation in domain generalization

Given the rise of large-scale training regimes, adapting pre-trained models
to a wide range of downstream tasks has become a standard approach in machine
learning. While large benefits in empirical performance have been observed, it
is not yet well understood how robustness properties transfer from a
pre-trained model to a downstream task. We prove that the robustness of a
predictor on downstream tasks can be bound by the robustness of its underlying
representation, irrespective of the pre-training protocol. Taken together, our
results precisely characterize what is required of the representation function
for reliable performance upon deployment.

在机器学习中，预训练模型对于下游任务的使用已经成为一种标准方法，虽然其实证明了效果提升，但是预训练模型的鲁棒性如何传递到下游任务中还未被充分理解。我们证明了预测器的鲁棒性可以通过其基础表示的鲁棒性来界定，与预训练协议无关。综上所述，我们的结果明确了可靠性表现在部署时所需要的表示函数的要求。

对抗训练的稳健性从预训练到下游任务的迁移

How Adversarial Robustness Transfers from Pre-training to Downstream Tasks

Machine learning systems are increasingly used to make decisions about
people's lives, such as whether to give someone a loan or whether to interview
someone for a job. This has led to considerable interest in making such machine
learning systems fair. One approach is to transform the input data used by the
algorithm. This can be achieved by passing each input data point through a
representation function prior to its use in training or testing. Techniques for
learning such representation functions from data have been successful
empirically, but typically lack theoretical fairness guarantees. We show that
it is possible to prove that a representation function is fair according to
common measures of both group and individual fairness, as well as useful with
respect to a target task. These provable properties can be used in a governance
model involving a data producer, a data user and a data regulator, where there
is a separation of concerns between fairness and target task utility to ensure
transparency and prevent perverse incentives. We formally define the 'cost of
mistrust' of using this model compared to the setting where there is a single
trusted party, and provide bounds on this cost in particular cases. We present
a practical approach to learning fair representation functions and apply it to
financial and criminal justice datasets. We evaluate the fairness and utility
of these representation functions using measures motivated by our theoretical
results.

本文介绍了一种基于代表函数的方法，证明了其可实现机器学习中的公平，适用于聚合模型，具有透明度和防止恶性激励的优势，并应用于金融和刑事司法数据集。