Daoyuan Wang

AAAI Conference 2026 Conference Paper

Geometry-Aware Variational Information Maximization for Deep Incomplete Multi-view Clustering

• Wenlan Chen
• Lu Gao
• Daoyuan Wang
• Fei Guo
• Cheng Liang

Incomplete multi-view clustering (IMVC) aims to group data into meaningful clusters when each sample is only partially observed across multiple views. Most existing methods either rely on imputation strategies that may introduce noise and distort the underlying data distribution, or adopt cross-view alignment techniques that focus on pairwise relationships, often resulting in suboptimal representations and unstable clustering performance. In this paper, we propose Geometry-Aware Variational Information Maximization for Deep Incomplete Multi-view Clustering (GAVIM), a novel imputation-free variational framework that enables robust and coherent incomplete multi-view clustering. Specifically, GAVIM leverages mutual information maximization to preserve the high mutual information between the available multi-view data and the shared embedding. Moreover, we explicitly retain local geometric consistency within each view-specific latent space under the guidance of an adaptive global supervision signal. Lastly, GAVIM aligns all views simultaneously using a Gramian representation alignment measure, ensuring coherent structure across modalities and promoting unified, semantically meaningful representations. Extensive experiments on five benchmark IMVC datasets with varying levels of view incompleteness demonstrate that GAVIM consistently outperforms state-of-the-art methods in clustering accuracy and representation quality.

NeurIPS Conference 2025 Conference Paper

Disentangled Cross-Modal Representation Learning with Enhanced Mutual Supervision

• Lu Gao
• Wenlan Chen
• Daoyuan Wang
• Fei Guo
• Cheng Liang

Cross-modal representation learning aims to extract semantically aligned representations from heterogeneous modalities such as images and text. Existing multimodal VAE-based models often suffer from limited capability to align heterogeneous modalities or lack sufficient structural constraints to clearly separate the modality-specific and shared factors. In this work, we propose a novel framework, termed D isentangled C ross- M odal Representation Learning with E nhanced M utual Supervision (DCMEM). Specifically, our model disentangles the common and distinct information across modalities and regularizes the shared representation learned from each modality in a mutually supervised manner. Moreover, we incorporate the information bottleneck principle into our model to ensure that the shared and modality-specific factors encode exclusive yet complementary information. Notably, our model is designed to be trainable on both complete and partial multimodal datasets with a valid Evidence Lower Bound. Extensive experimental results demonstrate significant improvements of our model over existing methods on various tasks including cross-modal generation, clustering, and classification.

IJCAI Conference 2025 Conference Paper

Image-Enhanced Hybrid Encoding with Reinforced Contrastive Learning for Spatial Domain Identification in Spatial Transcriptomics

• Daoyuan Wang
• Lu Gao
• Wenlan Chen
• Cheng Liang
• Fei Guo

Spatial transcriptomics integrates spatial, gene expression, and multichannel immunohistochemistry image data, enabling advanced insights into cellular organization. However, existing methods often struggle to effectively fuse these multimodal data, limiting their potential for accurate spatial domain identification. Here, we propose IE-HERCL (Image-Enhanced Hybrid Encoding with Reinforced Contrastive Learning), a novel framework designed to address this challenge. Specifically, IE-HERCL employs hybrid encoding to capture both the non-spatial features and spatial dependencies for both gene and image modalities via autoencoders and GraphSAGE, respectively. These features are then fused using cross-view attention mechanisms to generate the unified informative embedding. To enhance the representation learning capability, we introduce a reinforced contrastive learning strategy to mitigate the influences of false negative samples, where we detect potential positive counterparts with high-order random walks. In addition, the cluster alignment is dynamically refined through optimal transport, which ensures that the fused consensus representation is coherent and robust, enabling accurate spatial domain identification. Our approach achieves state-of-the-art performance on five image-enhanced spatial transcriptomics datasets, demonstrating its robustness and effectiveness in multimodal integration and spatial domain identification. IE-HERCL offers a powerful and innovative solution for advancing spatial transcriptomics analysis. The code is released on https: //github. com/wdyi701/IE-HERCL.

EAAI Journal 2025 Journal Article

Unsupervised multi-view feature selection based on weighted low-rank tensor learning and its application in multi-omics datasets

• Daoyuan Wang
• Lianzhi Wang
• Wenlan Chen
• Hong Wang
• Cheng Liang

JBHI Journal 2023 Journal Article

Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy

• Xue Feng
• Daoyuan Wang
• Qing Pan
• Molei Yan
• Xiaoqing Liu
• Yanfei Shen
• Luping Fang
• Guolong Cai

Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored. For the goal of reducing 28-day mortality of the patients undergoing NIV, Double Dueling Deep Q Network (D3QN) of offline-reinforcement learning algorithm was adopted to develop an optimal regime model for making treatment decisions of discontinuing ventilation, continuing NIV, or intubation. The model was trained and tested using the data from Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) and evaluated by the practical strategies. Furthermore, the applicability of the model in majority disease subgroups (Catalogued by International Classification of Diseases, ICD) was investigated. Compared with physician's strategies, the proposed model achieved a higher expected return score (4. 25 vs. 2. 68) and its recommended treatments reduced the expected mortality from 27. 82% to 25. 44% in all NIV cases. In particular, for these patients finally received intubation in practice, if the model also supported the regime, it would warn of switching to intubation 13. 36 hours earlier than clinicians (8. 64 vs. 22 hours after the NIV treatment), granting a 21. 7% reduction in estimated mortality. In addition, the model was applicable across various disease groups with distinguished achievement in dealing with respiratory disorders. The proposed model is promising to dynamically provide personalized optimal NIV switching regime for patients undergoing NIV with the potential of improving treatment outcomes.