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Xian Yang

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Adaptive Evolutionary Fusion for Multi-View Clustering

Yunxiao Zhao
Liang Bai
Xian Yang

Deep multi-view clustering (MVC) methods achieve impressive performance by effectively capturing complementary information across views, where feature fusion serves as the critical mechanism for maximizing cross-view complementarity. However, most existing methods suffer from rigid dependence on non-adaptive predefined fusion operations, resulting in unverifiable and potentially suboptimal fused feature quality. To resolve these limitations, we propose a novel multi-view clustering framework that learns adaptive hierarchical fusion through an unsupervised evolutionary algorithm. Unlike conventional predefined-fusion strategies, our approach employs tree-structured representations (Fusion Trees) for adaptive feature integration. These Fusion Trees are optimized via our evolutionary mechanism, in which models sharing identical architectures but distinct Fusion Trees are conceptualized as evolutionary individuals. Through implementation of the evolutionarily optimized Fusion Tree, the resultant model generates discriminative representations in accordance with biological evolutionary principles. Comprehensive benchmarking across twelve multi-view datasets validates significant performance gains improvement over state-of-the-art baselines.

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JBHI Journal 2026 Journal Article

Asymmetric Co-Training With Decoder–Head Decoupling for Semi-Supervised Medical Image Segmentation

Yuxin Tian
Muhan Shi
Jianxun Li
Bin Zhang
Min Qu
Yinxue Shi
Xian Yang
Min Wang

Semi-supervised learning reduces annotation costs in medical image segmentation by leveraging abundant unlabeled data alongside scarce labels. Most models adopt an encoder–decoder architecture with a task-specific segmentation head. While co-training is effective, existing frameworks suffer from intra-network coupling (decoder–head binding) and inter-network coupling (over-aligned predictions), which reduce prediction diversity and amplify confirmation bias–particularly for small structures, ambiguous boundaries, and anatomically variable regions. We propose AsyCo, an asymmetric co-training framework with two components. (1) Asymmetric Decoder Coupling implements decoder–head decoupling by dynamically remapping encoder–decoder features to non-default heads across branches, breaking intra-network coupling and creating diverse prediction paths without additional parameters. (2) Hierarchical Consistency Regularization converts this diversity into stable supervision by aligning (i) the two branches' final outputs along their default paths (branch-output consistency), (ii) predictions from different segmentation heads evaluated on identical decoder features (inter-head consistency), and (iii) intermediate encoder–decoder representations (representation consistency). Through these mechanisms, AsyCo explicitly mitigates both intra- and inter-network coupling, improving training stability and reducing confirmation bias. Extensive experiments on three clinical benchmarks under limited-label regimes demonstrate that AsyCo consistently outperforms nine state-of-the-art semi-supervised learning methods. These results indicate that AsyCo delivers accurate and reliable segmentation with minimal annotation, thereby enhancing the reliability of medical image analysis in real-world clinical practice.

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AAAI Conference 2026 Conference Paper

Attribute-guided Dynamic Prompt Learning for Graph Neural Networks

Zhuomin Liang
Liang Bai
Xian Yang

Graph Neural Networks (GNNs) have achieved remarkable success in analyzing graph-structured data, with their performance dependent on the graph structure. However, models trained on high-quality graph structures often suffer a significant performance drop when evaluated on perturbed graphs. Existing methods tackle this problem by improving the robustness of GNNs, but they often overlook representation deviation caused by structural changes. To address this limitation, we propose an attribute-guided dynamic prompt learning model that generates prompt vectors to approximate the intrinsic information of nodes. With these prompt vectors, the trained GNNs are expected to maintain their performance under perturbed graph structures. Unlike previous prompt-based methods that learn unified prompt vectors for all nodes, we obtain node-level prompts by encoding node attributes that provide unique information. Given the diversity of perturbed graph structures during inference, we introduce a structure-aware adaptation mechanism that adjusts the prompt vectors based on the input graph. Furthermore, we apply gradient-based attacks to generate perturbed graphs, encouraging the model to generalize to unseen structures. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness and robustness of our model.

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AAAI Conference 2026 Conference Paper

CauVQ: Causal Vector Quantization for Graph OOD Generalization

Weihong Zhang
Liang Bai
Hangyuan Du
Xian Yang

Graph Neural Networks (GNNs) perform well on in-distribution data but often fail under out-of-distribution (OOD) shifts due to reliance on spurious patterns. To address this, we propose CauVQ, a causal vector quantization framework that improves OOD generalization by identifying and leveraging invariant substructures that are causally predictive. To construct stable and symbolic graph representations, CauVQ decomposes each input into local substructures and maps them to a discrete codebook of prototypical motifs. This enables consistent and interpretable encoding across diverse graph domains. To isolate the causal substructures, we maximize their mutual information with graph labels and refine their representations using a learnable interaction matrix and a causal attention mechanism. Furthermore, we introduce a counterfactual regularization strategy to enforce prediction stability under substructure perturbations, encouraging the model to focus on truly causal patterns rather than superficial shortcuts. Extensive experiments across standard and OOD benchmarks demonstrate that CauVQ consistently outperforms state-of-the-art baselines in robustness and interpretability. Our framework offers a promising step toward reliable, explainable, and distribution-aware graph learning.

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AAAI Conference 2026 Conference Paper

Medical Vision–Language Pretraining with LLM-Guided Temporal Supervision

Liang Bai
Zhi Wang
Huimin Yan
Xian Yang

Medical vision–language pretraining typically relies on static image–text pairs, overlooking temporal cues vital for understanding clinical progression. This limits model sensitivity to evolving semantics and reduces their effectiveness in real-world clinical reasoning. To address this challenge, we propose TAMM—a temporal alignment framework that leverages weak but semantically rich supervision from large language models (LLMs). Given temporally adjacent clinical reports, LLMs automatically generate (i) coarse-grained trend labels (e.g., improving or worsening), and (ii) fine-grained rationales explaining the supporting clinical evidence. These complementary signals inject temporal semantics without requiring manual annotation, and guide vision–language representation learning to capture trend-sensitive cross-modal alignment and rationale-grounded coherence. Experiments on multiple medical benchmarks demonstrate that TAMM improves retrieval and classification performance while yielding more interpretable, temporally consistent embeddings. Our results highlight the potential of leveraging LLM-derived supervision to equip vision–language models with temporal awareness critical for clinical applications.

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UAI Conference 2025 Conference Paper

MSCGrapher: Learning Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting

Xian Yang
Zhenguo Zhang
Shihao Lu

Efficient learning intra-series and inter-series correlations is essential for multivariate time series forecasting (MTSF). However, in real-world scenarios, persistent and significant inter-series correlations are challenging to be represented in a static way and the strength of correlations varies across different time scales. In this paper, we address this challenge by modeling the complex inter-series relationships through dynamical correlations, considering the varying strengths of correlations. We propose a novel MTSF model: MSCGrapher, which leverages an adaptive correlation learning block to uncover inter-series correlations across different scales. Concretely, time series are first decomposed into different scales based on their periodicities. The graph representation of MTS is then constructed and an adaptive correlation learning method is introduced to capture the inter-series correlations across different scales. To quantify the strength of these correlations, we compute correlation scores based on the characteristics of the graph edges and classify correlations as either $\textit{Strong}$ or $\textit{Weak}$. Finally, we employ a self-attention module to capture intra-series correlations and then fuse features from all scales to obtain the final representation. Extensive experiments on 12 real-world datasets show that MSCGrapher gains significant forecasting performance, highlighting the critical role of inter-series correlations in capturing implicit patterns for MTS.

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