Data shift is a phenomenon present in many real-world applications, and while
there are multiple methods attempting to detect shifts, the task of localizing
and correcting the features originating such shifts has not been studied in
depth. Feature shifts can occur in many datasets, including in multi-sensor
data, where some sensors are malfunctioning, or in tabular and structured data,
including biomedical, financial, and survey data, where faulty standardization
and data processing pipelines can lead to erroneous features. In this work, we
explore using the principles of adversarial learning, where the information
from several discriminators trained to distinguish between two distributions is
used to both detect the corrupted features and fix them in order to remove the
distribution shift between datasets. We show that mainstream supervised
classifiers, such as random forest or gradient boosting trees, combined with
simple iterative heuristics, can localize and correct feature shifts,
outperforming current statistical and neural network-based techniques. The code
is available at this https URL

利用对抗性学习的原则，结合主流的监督分类器和简单迭代启发式方法，本研究探索了利用多个辨别器的信息来检测和修复数据集之间特征变化，以消除特征变化所带来的分布偏移，优于当前的统计和神经网络技术。

特征偏移检测与校正的对抗学习

Adversarial Learning for Feature Shift Detection and Correction

The transfer of models trained on labeled datasets in a source domain to
unlabeled target domains is made possible by unsupervised domain adaptation
(UDA). However, when dealing with complex time series models, the
transferability becomes challenging due to the dynamic temporal structure that
varies between domains, resulting in feature shifts and gaps in the time and
frequency representations. Furthermore, tasks in the source and target domains
can have vastly different label distributions, making it difficult for UDA to
mitigate label shifts and recognize labels that only exist in the target
domain. We present RAINCOAT, the first model for both closed-set and universal
DA on complex time series. RAINCOAT addresses feature and label shifts by
considering both temporal and frequency features, aligning them across domains,
and correcting for misalignments to facilitate the detection of private labels.
Additionally,RAINCOAT improves transferability by identifying label shifts in
target domains. Our experiments with 5 datasets and 13 state-of-the-art UDA
methods demonstrate that RAINCOAT can achieve an improvement in performance of
up to 16.33%, and can effectively handle both closed-set and universal
adaptation.

利用复杂时间序列建立了第一个用于封闭集和通用领域不受监督域自适应的深度学习模型 RAINCOAT，可实现跨领域的时频特征对齐并通过识别标签偏移来解决标签偏移问题，最多可以提高 16.33% 的性能表现。

特征与标签变化下的时间序列领域自适应

Domain Adaptation for Time Series Under Feature and Label Shifts

It is often critical for prediction models to be robust to distributional
shifts between training and testing data. From a causal perspective, the
challenge is to distinguish the stable causal relationships from the unstable
spurious correlations across shifts. We describe a causal transfer random
forest (CTRF) that combines existing training data with a small amount of data
from a randomized experiment to train a model which is robust to the feature
shifts and therefore transfers to a new targeting distribution. Theoretically,
we justify the robustness of the approach against feature shifts with the
knowledge from causal learning. Empirically, we evaluate the CTRF using both
synthetic data experiments and real-world experiments in the Bing Ads platform,
including a click prediction task and in the context of an end-to-end
counterfactual optimization system. The proposed CTRF produces robust
predictions and outperforms most baseline methods compared in the presence of
feature shifts.

提出了一种 causal transfer random forest 模型，它将原有的训练数据与来自一个随机实验的少量数据组合，使其对 feature shift 具有鲁棒性，并在点击预测等任务中表现出优越性。