Data imputation, the process of filling in missing feature elements for
incomplete data sets, plays a crucial role in data-driven learning. A
fundamental belief is that data imputation is helpful for learning performance,
and it follows that the pursuit of better classification can guide the data
imputation process. While some works consider using label information to assist
in this task, their simplistic utilization of labels lacks flexibility and may
rely on strict assumptions. In this paper, we propose a new framework that
effectively leverages supervision information to complete missing data in a
manner conducive to classification. Specifically, this framework operates in
two stages. Firstly, it leverages labels to supervise the optimization of
similarity relationships among data, represented by the kernel matrix, with the
goal of enhancing classification accuracy. To mitigate overfitting that may
occur during this process, a perturbation variable is introduced to improve the
robustness of the framework. Secondly, the learned kernel matrix serves as
additional supervision information to guide data imputation through regression,
utilizing the block coordinate descent method. The superiority of the proposed
method is evaluated on four real-world data sets by comparing it with
state-of-the-art imputation methods. Remarkably, our algorithm significantly
outperforms other methods when the data is missing more than 60\% of the
features

提出一种新的框架，通过利用监督信息来完成缺失数据，使其有利于分类，并在四个真实数据集上证明了该方法的优越性能。

追求更好分类的数据插补：一种监督式基于核函数的方法

Data Imputation by Pursuing Better Classification: A Supervised  Kernel-Based Method

Label noise widely exists in large-scale datasets and significantly
degenerates the performances of deep learning algorithms. Due to the
non-identifiability of the instance-dependent noise transition matrix, most
existing algorithms address the problem by assuming the noisy label generation
process to be independent of the instance features. Unfortunately, noisy labels
in real-world applications often depend on both the true label and the
features. In this work, we tackle instance-dependent label noise with a novel
deep generative model that avoids explicitly modeling the noise transition
matrix. Our algorithm leverages casual representation learning and
simultaneously identifies the high-level content and style latent factors from
the data. By exploiting the supervision information of noisy labels with
structural causal models, our empirical evaluations on a wide range of
synthetic and real-world instance-dependent label noise datasets demonstrate
that the proposed algorithm significantly outperforms the state-of-the-art
counterparts.

本文介绍了基于结构因果模型的深度生成模型算法，解决了大规模数据集中存在的标签噪声问题。该算法有效利用了噪声标签的监督信息，并在合成和实际的噪声标签数据集上表现优异。

重新思考标签价值：针对实例相关的标签噪声学习

Rethinking the Value of Labels for Instance-Dependent Label Noise  Learning

Topology-imbalance is a graph-specific imbalance problem caused by the uneven
topology positions of labeled nodes, which significantly damages the
performance of GNNs. What topology-imbalance means and how to measure its
impact on graph learning remain under-explored. In this paper, we provide a new
understanding of topology-imbalance from a global view of the supervision
information distribution in terms of under-reaching and over-squashing, which
motivates two quantitative metrics as measurements. In light of our analysis,
we propose a novel position-aware graph structure learning framework named
PASTEL, which directly optimizes the information propagation path and solves
the topology-imbalance issue in essence. Our key insight is to enhance the
connectivity of nodes within the same class for more supervision information,
thereby relieving the under-reaching and over-squashing phenomena.
Specifically, we design an anchor-based position encoding mechanism, which
better incorporates relative topology position and enhances the intra-class
inductive bias by maximizing the label influence. We further propose a
class-wise conflict measure as the edge weights, which benefits the separation
of different node classes. Extensive experiments demonstrate the superior
potential and adaptability of PASTEL in enhancing GNNs' power in different data
annotation scenarios.

本文提出了一种新的全局视角的监督信息分布量度 --- 欠拟合和过拟合，并以此为基础提出了 PASTEL 框架。此框架通过加强同一类别节点间的连接以增加监督信息，解决了局部拓扑位置不平衡的问题，能够在不同的数据标注场景下提升 GNNs 的性能。

通过缓解低伸展和过度压缩来缓解图拓扑不平衡的位置感知结构学习

Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing

A complementary label (CL) simply indicates an incorrect class of an example,
but learning with CLs results in multi-class classifiers that can predict the
correct class. Unfortunately, the problem setting only allows a single CL for
each example, which notably limits its potential since our labelers may easily
identify multiple CLs (MCLs) to one example. In this paper, we propose a novel
problem setting to allow MCLs for each example and two ways for learning with
MCLs. In the first way, we design two wrappers that decompose MCLs into many
single CLs, so that we could use any method for learning with CLs. However, the
supervision information that MCLs hold is conceptually diluted after
decomposition. Thus, in the second way, we derive an unbiased risk estimator;
minimizing it processes each set of MCLs as a whole and possesses an estimation
error bound. We further improve the second way into minimizing properly chosen
upper bounds. Experiments show that the former way works well for learning with
MCLs but the latter is even better.

本文提出一种新的问题设置方法，允许每个例子有多个补充标签（MCLs）并探讨两种学习 MCLs 的方法：对 MCLs 进行分解以便使用任何带有 CLs 的学习方法，或者使用一个无偏风险估计值并将每组 MCLs 一起处理.