Empirical risk minimization (ERM) is sensitive to spurious correlations in
the training data, which poses a significant risk when deploying systems
trained under this paradigm in high-stake applications. While the existing
literature focuses on maximizing group-balanced or worst-group accuracy,
estimating these accuracies is hindered by costly bias annotations. This study
contends that current bias-unsupervised approaches to group robustness continue
to rely on group information to achieve optimal performance. Firstly, these
methods implicitly assume that all group combinations are represented during
training. To illustrate this, we introduce a systematic generalization task on
the MPI3D dataset and discover that current algorithms fail to improve the ERM
baseline when combinations of observed attribute values are missing. Secondly,
bias labels are still crucial for effective model selection, restricting the
practicality of these methods in real-world scenarios. To address these
limitations, we propose a revised methodology for training and validating
debiased models in an entirely bias-unsupervised manner. We achieve this by
employing pretrained self-supervised models to reliably extract bias
information, which enables the integration of a logit adjustment training loss
with our validation criterion. Our empirical analysis on synthetic and
real-world tasks provides evidence that our approach overcomes the identified
challenges and consistently enhances robust accuracy, attaining performance
which is competitive with or outperforms that of state-of-the-art methods,
which, conversely, rely on bias labels for validation.

这项研究提出了一个修订的方法来在完全无偏见的情况下进行训练和验证无偏见模型，通过采用预训练的自监督模型可靠地提取偏见信息，并与我们的验证准则结合使用逻辑调整训练损失函数。我们的实证分析证明了我们的方法克服了所发现的挑战，始终提高了鲁棒准确性，达到与或优于依赖偏见标签进行验证的最先进方法的性能。

无任何群组信息的群组鲁棒分类

Group Robust Classification Without Any Group Information

Recent work has shown that standard training via empirical risk minimization
(ERM) can produce models that achieve high accuracy on average but low accuracy
on underrepresented groups due to the prevalence of spurious features. A
predominant approach to tackle this group robustness problem minimizes the
worst group error (akin to a minimax strategy) on the training data, hoping it
will generalize well on the testing data. However, this is often suboptimal,
especially when the out-of-distribution (OOD) test data contains previously
unseen groups. Inspired by ideas from the information retrieval and
learning-to-rank literature, this paper first proposes to use Discounted
Cumulative Gain (DCG) as a metric of model quality for facilitating better
hyperparameter tuning and model selection. Being a ranking-based metric, DCG
weights multiple poorly-performing groups (instead of considering just the
group with the worst performance). As a natural next step, we build on our
results to propose a ranking-based training method called Discounted Rank
Upweighting (DRU), which differentially reweights a ranked list of
poorly-performing groups in the training data to learn models that exhibit
strong OOD performance on the test data. Results on several synthetic and
real-world datasets highlight the superior generalization ability of our
group-ranking-based (akin to soft-minimax) approach in selecting and learning
models that are robust to group distributional shifts.

本文提出了一种基于 Discounted Cumulative Gain（DCG）评估模型质量的方法，并建议了 Discounted Rank Upweighting（DRU）训练方法以提高模型的品质，在实验中结果表明，这种基于组排名的训练方法能够选择和学习出对组分布性变化具有鲁棒性的模型。