Uplift modeling has shown very promising results in online marketing.
However, most existing works are prone to the robustness challenge in some
practical applications. In this paper, we first present a possible explanation
for the above phenomenon. We verify that there is a feature sensitivity problem
in online marketing using different real-world datasets, where the perturbation
of some key features will seriously affect the performance of the uplift model
and even cause the opposite trend. To solve the above problem, we propose a
novel robustness-enhanced uplift modeling framework with adversarial feature
desensitization (RUAD). Specifically, our RUAD can more effectively alleviate
the feature sensitivity of the uplift model through two customized modules,
including a feature selection module with joint multi-label modeling to
identify a key subset from the input features and an adversarial feature
desensitization module using adversarial training and soft interpolation
operations to enhance the robustness of the model against this selected subset
of features. Finally, we conduct extensive experiments on a public dataset and
a real product dataset to verify the effectiveness of our RUAD in online
marketing. In addition, we also demonstrate the robustness of our RUAD to the
feature sensitivity, as well as the compatibility with different uplift models.

提出了一种新的强化鲁棒性的提升建模框架 RUAD，通过两个定制模块，即联合多标签建模的特征选择模块和利用对抗训练和软插值操作的对抗性特征去敏模块，更有效地缓解提升模型的特征敏感性，从而提高在线营销中的效果和鲁棒性。

具有对抗特征去敏感化的鲁棒性增强提升建模

Robustness-enhanced Uplift Modeling with Adversarial Feature  Desensitization

Test-Time Adaptation (TTA) has recently emerged as a promising approach for
tackling the robustness challenge under distribution shifts. However, the lack
of consistent settings and systematic studies in prior literature hinders
thorough assessments of existing methods. To address this issue, we present
TTAB, a test-time adaptation benchmark that encompasses ten state-of-the-art
algorithms, a diverse array of distribution shifts, and two evaluation
protocols. Through extensive experiments, our benchmark reveals three common
pitfalls in prior efforts. First, selecting appropriate hyper-parameters,
especially for model selection, is exceedingly difficult due to online batch
dependency. Second, the effectiveness of TTA varies greatly depending on the
quality and properties of the model being adapted. Third, even under optimal
algorithmic conditions, none of the existing methods are capable of addressing
all common types of distribution shifts. Our findings underscore the need for
future research in the field to conduct rigorous evaluations on a broader set
of models and shifts, and to re-examine the assumptions behind the empirical
success of TTA. Our code is available at
https://github.com/lins-lab/ttab.

本文提出了针对测试时间适应（TTA）的测试基准 TTAB，通过实验表明：在线批次依赖性使得选择适当的超参数尤其是选择模型极为困难，TTA 的有效性取决于模型的质量和属性，并且即使在最优算法条件下，现有方法也无法处理所有常见类型的分布转移，因此需要在更广泛的模型和转移集上进行严格评估和重新审视 TTA 的实证成功条件。

关于测试时适应性的陷阱

On Pitfalls of Test-Time Adaptation

Large language models (LLMs) have achieved state-of-the-art performance on a
series of natural language understanding tasks. However, these LLMs might rely
on dataset bias and artifacts as shortcuts for prediction. This has
significantly hurt their Out-of-Distribution (OOD) generalization and
adversarial robustness. In this paper, we provide a review of recent
developments that address the robustness challenge of LLMs. We first introduce
the concepts and robustness challenge of LLMs. We then introduce methods to
identify shortcut learning behavior in LLMs, characterize the reasons for
shortcut learning, as well as introduce mitigation solutions. Finally, we
identify key challenges and introduce the connections of this line of research
to other directions.

本文介绍了大型语言模型的概念、挑战和解决方法，着重关注了数据集偏差和简化学习对其抗干扰性的影响，提出了识别和缓解这些影响的方法，并探讨了未来可能的研究方向。