Multi-view learning methods often focus on improving decision accuracy while
neglecting the decision uncertainty, which significantly restricts their
applications in safety-critical applications. To address this issue,
researchers propose trusted multi-view methods that learn the class
distribution for each instance, enabling the estimation of classification
probabilities and uncertainty. However, these methods heavily rely on
high-quality ground-truth labels. This motivates us to delve into a new
generalized trusted multi-view learning problem: how to develop a reliable
multi-view learning model under the guidance of noisy labels? We propose a
trusted multi-view noise refining method to solve this problem. We first
construct view-opinions using evidential deep neural networks, which consist of
belief mass vectors and uncertainty estimates. Subsequently, we design
view-specific noise correlation matrices that transform the original opinions
into noisy opinions aligned with the noisy labels. Considering label noises
originating from low-quality data features and easily-confused classes, we
ensure that the diagonal elements of these matrices are inversely proportional
to the uncertainty, while incorporating class relations into the off-diagonal
elements. Finally, we aggregate the noisy opinions and employ a generalized
maximum likelihood loss on the aggregated opinion for model training, guided by
the noisy labels. We empirically compare TMNR with state-of-the-art trusted
multi-view learning and label noise learning baselines on 5 publicly available
datasets. Experiment results show that TMNR outperforms baseline methods on
accuracy, reliability and robustness. We promise to release the code and all
datasets on Github and show the link here.

通过引入噪音标签，我们提出了一种可靠的多视图学习方法，该方法能够学习每个实例的类分布，估计分类概率和不确定性，并且在准确性、可靠性和鲁棒性方面优于基线方法。

带有标签噪音的可信多视图学习

Trusted Multi-view Learning with Label Noise

Multi-view learning aims to combine multiple features to achieve more
comprehensive descriptions of data. Most previous works assume that multiple
views are strictly aligned. However, real-world multi-view data may contain
low-quality conflictive instances, which show conflictive information in
different views. Previous methods for this problem mainly focus on eliminating
the conflictive data instances by removing them or replacing conflictive views.
Nevertheless, real-world applications usually require making decisions for
conflictive instances rather than only eliminating them. To solve this, we
point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which
requires the model to provide decision results and attached reliabilities for
conflictive multi-view data. We develop an Evidential Conflictive Multi-view
Learning (ECML) method for this problem. ECML first learns view-specific
evidence, which could be termed as the amount of support to each category
collected from data. Then, we can construct view-specific opinions consisting
of decision results and reliability. In the multi-view fusion stage, we propose
a conflictive opinion aggregation strategy and theoretically prove this
strategy can exactly model the relation of multi-view common and view-specific
reliabilities. Experiments performed on 6 datasets verify the effectiveness of
ECML.

提出了一种可靠性冲突多视图学习 (RCML) 问题，并提出了 Evidential Conflictive Multi-view Learning (ECML) 方法，该方法首先学习视图特定证据，然后构建视图特定观点，最后在多视图融合阶段提出了冲突观点聚合策略。实验证实了 ECML 的有效性。

可靠的矛盾多视角学习

Reliable Conflictive Multi-View Learning

The aggregation of multiple opinions plays a crucial role in decision-making,
such as in hiring and loan review, and in labeling data for supervised
learning. Although majority voting and existing opinion aggregation models are
effective for simple tasks, they are inappropriate for tasks without
objectively true labels in which disagreements may occur. In particular, when
voter attributes such as gender or race introduce bias into opinions, the
aggregation results may vary depending on the composition of voter attributes.
A balanced group of voters is desirable for fair aggregation results but may be
difficult to prepare. In this study, we consider methods to achieve fair
opinion aggregation based on voter attributes and evaluate the fairness of the
aggregated results. To this end, we consider an approach that combines opinion
aggregation models such as majority voting and the Dawid and Skene model (D&S
model) with fairness options such as sample weighting. To evaluate the fairness
of opinion aggregation, probabilistic soft labels are preferred over discrete
class labels. First, we address the problem of soft label estimation without
considering voter attributes and identify some issues with the D&S model. To
address these limitations, we propose a new Soft D&S model with improved
accuracy in estimating soft labels. Moreover, we evaluated the fairness of an
opinion aggregation model, including Soft D&S, in combination with different
fairness options using synthetic and semi-synthetic data. The experimental
results suggest that the combination of Soft D&S and data splitting as a
fairness option is effective for dense data, whereas weighted majority voting
is effective for sparse data. These findings should prove particularly valuable
in supporting decision-making by human and machine-learning models with
balanced opinion aggregation.

使用包括意见聚合和公平选择在内的方法，平衡持不同意见的投票者属性以实现公正的意见聚合，并使用软标签评估聚合结果的公正性。

减轻选民属性偏差以实现公正的意见聚合

Mitigating Voter Attribute Bias for Fair Opinion Aggregation

Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that
assist users in accomplishing specific goals, such as booking a hotel or a
restaurant. Traditional TODs rely on domain-specific APIs/DBs or external
factual knowledge to generate responses, which cannot accommodate subjective
user requests (e.g., "Is the WIFI reliable?" or "Does the restaurant have a
good atmosphere?"). To address this issue, we propose a novel task of
subjective-knowledge-based TOD (SK-TOD). We also propose the first
corresponding dataset, which contains subjective knowledge-seeking dialogue
contexts and manually annotated responses grounded in subjective knowledge
sources. When evaluated with existing TOD approaches, we find that this task
poses new challenges such as aggregating diverse opinions from multiple
knowledge snippets. We hope this task and dataset can promote further research
on TOD and subjective content understanding. The code and the dataset are
available at this https URL

本篇论文提出了主观知识为基础的任务导向对话系统（SK-TOD）的新任务，以及配套的数据集，该数据集包含了主观知识寻求对话上下文和手动注释的响应，并探究了多个知识片段的不同观点如何汇聚的问题，以期促进 TOD 和主观内容理解的进一步研究。