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Xiaowei Wang

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Robust High-Order Tensor Compressive Sensing Based on M-Estimators

Xiaowei Wang
Jie Yu
Yulong Wang

Tensor Compressive Sensing (TCS) has gained significant attention recently due to its strong ability to preserve the multidimensional structure of data. However, existing TCS methods face three critical challenges: 1) Biased approximation of tensor rank imposed by the convex surrogate Tensor Nuclear Norm (TNN) may interfere with the original low-rank structure of tensor data. 2) Vulnerability to non-Gaussian noise and outliers makes TCS methods highly susceptible to complex noise environments ubiquitous in real-world applications. 3) Most of them are confined to third-order tensors and cannot handle high-order tensor data effectively. Being aware of these, we propose Robust Tensor Compressive Sensing (RTCS) based on M-estimators with three key innovations: 1) We design a novel M-estimator-based low-rank regularizer for high-order tensors, which provides a superior approximation of tensor rank and better preserves the original data structure. 2) RTCS incorporates a robust Welsch estimator that adaptively mitigates the influence of complex noises and outliers in tensor recovery. 3) RTCS is developed to handle high-order tensors, thereby allowing for broader applicability beyond conventional third-order tensors. We further design an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to handle the complex optimization problem. Experiments show that RTCS consistently outperforms existing approaches across various noises.

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EAAI Journal 2025 Journal Article

A computation-efficient network with feature aggregation for cancer subtype classification on histopathological images

Zong Fan
Chaojie Zhang
Lulu Sun
Wade Thorstad
Hiram Gay
Xiaowei Wang
Hua Li

Histopathology whole-slide images (WSI) capture detailed structural and morphological features of tumor tissue, offering rich histological and molecular information. Deep learning (DL) methods have emerged to assist in automatically examining histopathology WSIs and supporting tumor classification. Traditional DL approaches for WSI images face challenges due to the intrinsic complexity of tumor tissue characteristics and the extremely large image size. Multiple instance learning (MIL) methods have been proposed to address these issues by splitting the WSI images into small non-overlapping tiles and aggregating predictions from selected informative tiles for the final classification outcome. However, MIL methods still face challenges such as the need for accurate pseudo-labels, the risk of losing local information, or the failure to learn explicit class-relevant information. To address these limitations, we propose a novel framework that uses a lightweight convolutional neural network (CNN)-based tile encoder (CTE) to extract local tile features and a Transformer-based feature aggregator (TFA) to fuse local features into a representative global feature for WSI classification. Three key contributions of our framework are as follows. Firstly, we design a two-stage training strategy that decouples a lightweight CTE pre-training (using sparsely sampled tiles for efficiency) and TFA fine-tuning (using all tiles for accuracy). It significantly reduces computational costs compared to existing MIL methods while alleviating local information loss. Secondly, dynamic self-attention-based aggregation is designed in TFA, leveraging the Transformer’s self-attention to weigh all local tile features without accurate pseudo-labels. It ensures comprehensive integration of local information from both tumor and ambiguous non-tumor regions to enrich global representations from input WSIs, which benefits the final classification performance. Finally, interpretable saliency maps are generated from TFA attention scores, highlighting histopathologically relevant regions to align model decisions with clinical reasoning. Comprehensive experiments on three cancer subtype datasets demonstrate the effectiveness of our proposed method over existing MIL approaches. We also conduct further investigations into the impacts of various factors on model performance, gaining in-depth insights into our method. Our framework achieves higher classification accuracy while maintaining computational efficiency, making it a promising tool for histopathology image analysis.

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ICLR Conference 2020 Conference Paper

Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities

Baichuan Yuan
Xiaowei Wang
Jianxin Ma
Chang Zhou
Andrea L. Bertozzi
Hongxia Yang

Multivariate spatial point process models can describe heterotopic data over space. However, highly multivariate intensities are computationally challenging due to the curse of dimensionality. To bridge this gap, we introduce a declustering based hidden variable model that leads to an efficient inference procedure via a variational autoencoder (VAE). We also prove that this model is a generalization of the VAE-based model for collaborative filtering. This leads to an interesting application of spatial point process models to recommender systems. Experimental results show the method's utility on both synthetic data and real-world data sets.

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