Corruptions due to data perturbations and label noise are prevalent in the
datasets from unreliable sources, which poses significant threats to model
training. Despite existing efforts in developing robust models, current
learning methods commonly overlook the possible co-existence of both
corruptions, limiting the effectiveness and practicability of the model.
In this paper, we develop an Effective and Robust Adversarial Training (ERAT)
framework to simultaneously handle two types of corruption (i.e., data and
label) without prior knowledge of their specifics. We propose a hybrid
adversarial training surrounding multiple potential adversarial perturbations,
alongside a semi-supervised learning based on class- rebalancing sample
selection to enhance the resilience of the model for dual corruption. On the
one hand, in the proposed adversarial training, the perturbation generation
module learns multiple surrogate malicious data perturbations by taking a DNN
model as the victim, while the model is trained to maintain semantic
consistency between the original data and the hybrid perturbed data. It is
expected to enable the model to cope with unpredictable perturbations in
real-world data corruption. On the other hand, a class-rebalancing data
selection strategy is designed to fairly differentiate clean labels from noisy
labels. Semi-supervised learning is performed accordingly by discarding noisy
labels. Extensive experiments demonstrate the superiority of the proposed ERAT
framework.

我們提出了一種有效且強健的對抗訓練 (ERAT) 框架，能夠同時處理兩類污染 (即數據和標籤)，而不需要事先了解其具體信息。我們通過混合對抗訓練和基於類平衡樣本選擇的半監督學習提高了模型對雙重污染的韌性。大量實驗證明了我們提出的 ERAT 框架的優越性。

有效且稳健的对抗训练以应对数据和标签损坏

Effective and Robust Adversarial Training against Data and Label  Corruptions

Quantifying variable importance is essential for answering high-stakes
questions in fields like genetics, public policy, and medicine. Current methods
generally calculate variable importance for a given model trained on a given
dataset. However, for a given dataset, there may be many models that explain
the target outcome equally well; without accounting for all possible
explanations, different researchers may arrive at many conflicting yet equally
valid conclusions given the same data. Additionally, even when accounting for
all possible explanations for a given dataset, these insights may not
generalize because not all good explanations are stable across reasonable data
perturbations. We propose a new variable importance framework that quantifies
the importance of a variable across the set of all good models and is stable
across the data distribution. Our framework is extremely flexible and can be
integrated with most existing model classes and global variable importance
metrics. We demonstrate through experiments that our framework recovers
variable importance rankings for complex simulation setups where other methods
fail. Further, we show that our framework accurately estimates the true
importance of a variable for the underlying data distribution. We provide
theoretical guarantees on the consistency and finite sample error rates for our
estimator. Finally, we demonstrate its utility with a real-world case study
exploring which genes are important for predicting HIV load in persons with
HIV, highlighting an important gene that has not previously been studied in
connection with HIV. Code is available here.

量化变量的重要性对于回答遗传学、公共政策和医学等领域的重要问题至关重要。我们提出了一个新的变量重要性框架，可以跨越所有好模型的集合，稳定地衡量变量的重要性，并在数据分布上准确估计变量的真实重要性。