学术讲座

大数据管理与分析方法研究北京市重点实验室（BDAI）研究生沙龙由中国人民大学高瓴人工智能学院师生组织定期举行。10月26日研讨会由学院魏哲巍教授指导的博士生郭雨荷和刘勇准聘副教授指导的博士生唐华镱分别介绍自己的研究工作。欢迎同学们积极参与研讨！

报告题目：Clenshaw Graph Neural Networks

讲者：郭雨荷，博士二年级 导师：魏哲巍

研究方向：图神经网络，图谱理论

Abstract: Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations. Recent GCN models use various residual connection techniques to alleviate the model degradation problem such as over-smoothing and gradient vanishing. Existing residual connection techniques, however, fail to make extensive use of underlying graph structure as in the graph spectral domain, which is critical for obtaining satisfactory results on heterophilic graphs. In this paper, we introduce ClenshawGCN, a GNN model that employs the Clenshaw Summation Algorithm to enhance the expressiveness of the GCN model. ClenshawGCN equips the standard GCN model with two straightforward residual modules: the adaptive initial residual connection and the negative second-order residual connection. We show that by adding these two residual modules, ClenshawGNN implicitly simulates a polynomial filter under the Chebyshev basis, giving it at least as much expressive power as polynomial spectral GNNs. In addition, we conduct comprehensive experiments to demonstrate the superiority of our model over spatial and spectral GNN models.

报告题目：Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm.

讲者：唐华镱，博士一年级，导师：刘勇

研究方向：无监督学习

Abstract :Incomplete multi-view clustering is a significant but challenging task. Although jointly imputing incomplete samples and conducting clustering has been shown to achieve promising performance, learning from both complete and incomplete data may be worse than learning only from complete data, particularly when imputed views are semantic inconsistent with missing views. To address this issue, we propose a novel framework to reduce the clustering performance degradation risk from semantic inconsistent imputed views. Concretely, by the proposed bi-level optimization framework, missing views are dynamically imputed from the learned semantic neighbors, and imputed samples are automatically selected for training. In theory, the empirical risk of the model is no higher than learning only from complete data, and the model is never worse than learning only from complete data in terms of expected risk with high probability. Comprehensive experiments demonstrate that the proposed method achieves superior performance and efficient safe incomplete multi-view clustering.