Graph Neural Networks (GNNs) have emerged as potent tools for predicting
outcomes in graph-structured data. Despite their efficacy, a significant
drawback of GNNs lies in their limited ability to provide robust uncertainty
estimates, posing challenges to their reliability in contexts where errors
carry significant consequences. Moreover, GNNs typically excel in
in-distribution settings, assuming that training and test data follow identical
distributions: a condition often unmet in real-world graph data scenarios. In
this article, we leverage conformal prediction, a widely recognized statistical
technique for quantifying uncertainty by transforming predictive model outputs
into prediction sets, to address uncertainty quantification in GNN predictions
amidst conditional shift \footnote{Representing the change in conditional
probability distribution $P(label |input)$ from source domain to target
domain.} in graph-based semi-supervised learning (SSL). Additionally, we
propose a novel loss function aimed at refining model predictions by minimizing
conditional shift in latent stages. Termed Conditional Shift Robust (CondSR)
conformal prediction for GNNs, our approach CondSR is model-agnostic and
adaptable to various classification models. We validate the effectiveness of
our method on standard graph benchmark datasets, integrating it with
state-of-the-art GNNs in node classification tasks. The code implementation is
publicly available for further exploration and experimentation.

使用符合预测（conformal prediction）技术，此论文提出了一种用于解决图半监督学习中图神经网络（GNN）预测不确定性问题的方法，该方法还引入了一种新的损失函数来优化模型预测，并在标准图基准数据集上进行了验证。

图神经网络的条件偏移鲁棒一致性预测

Conditional Shift-Robust Conformal Prediction for Graph Neural Network

Domain generalization (DG) aims to learn a model on several source domains,
hoping that the model can generalize well to unseen target domains. The
distribution shift between domains contains the covariate shift and conditional
shift, both of which the model must be able to handle for better
generalizability. In this paper, a novel DG method is proposed to deal with the
distribution shift via Visual Alignment and Uncertainty-guided belief Ensemble
(VAUE). Specifically, for the covariate shift, a visual alignment module is
designed to align the distribution of image style to a common empirical
Gaussian distribution so that the covariate shift can be eliminated in the
visual space. For the conditional shift, we adopt an uncertainty-guided belief
ensemble strategy based on the subjective logic and Dempster-Shafer theory. The
conditional distribution given a test sample is estimated by the dynamic
combination of that of source domains. Comprehensive experiments are conducted
to demonstrate the superior performance of the proposed method on four widely
used datasets, i.e., Office-Home, VLCS, TerraIncognita, and PACS.

本文提出一种新颖的领域泛化方法，通过视觉对齐和基于不确定性引导的置信度集成 (VAUE) 来处理分布偏移，在 4 个广泛使用的数据集上展示了其卓越的性能。

缓解协变量和条件偏移的领域泛化

Mitigating Both Covariate and Conditional Shift for Domain Generalization

Due to the ability of deep neural nets to learn rich representations, recent
advances in unsupervised domain adaptation have focused on learning
domain-invariant features that achieve a small error on the source domain. The
hope is that the learnt representation, together with the hypothesis learnt
from the source domain, can generalize to the target domain. In this paper, we
first construct a simple counterexample showing that, contrary to common
belief, the above conditions are not sufficient to guarantee successful domain
adaptation. In particular, the counterexample exhibits \emph{conditional
shift}: the class-conditional distributions of input features change between
source and target domains. To give a sufficient condition for domain
adaptation, we propose a natural and interpretable generalization upper bound
that explicitly takes into account the aforementioned shift. Moreover, we shed
new light on the problem by proving an information-theoretic lower bound on the
joint error of \emph{any} domain adaptation method that attempts to learn
invariant representations. Our result characterizes a fundamental tradeoff
between learning invariant representations and achieving small joint error on
both domains when the marginal label distributions differ from source to
target. Finally, we conduct experiments on real-world datasets that corroborate
our theoretical findings. We believe these insights are helpful in guiding the
future design of domain adaptation and representation learning algorithms.

本文针对无监督域适应学习中常见的假设提出了简单反例，证明了这些假设并不足以保证域适应的成功；提出了一种基于信息理论的弱化条件，并证明了任何试图学习不变表示的域适应方法的联合误差都存在一种基本的权衡；最后，通过实验验证了理论结果。