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Zhidong Li

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection

Rongcheng Wu
Hao Zhu
Shiying Zhang
Mingzhe Wang
Zhidong Li
Hui Li
Jianlong Zhou
Jiangtao Cui

Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass decoding fails to effectively handle anomalies with varying severity and scale. We propose a recursive architecture for autoencoder (RcAE), which performs reconstruction iteratively to progressively suppress anomalies while refining normal structures. Unlike traditional single-pass models, this recursive design naturally produces a sequence of reconstructions, progressively exposing suppressed abnormal patterns. To leverage this reconstruction dynamics, we introduce a Cross Recursion Detection (CRD) module that tracks inconsistencies across recursion steps, enhancing detection of both subtle and large-scale anomalies. Additionally, we incorporate a Detail Preservation Network (DPN) to recover high-frequency textures typically lost during reconstruction. Extensive experiments demonstrate that our method significantly outperforms existing non-diffusion methods, and achieves performance on par with recent diffusion models with only 10% of their parameters and offering substantially faster inference. These results highlight the practicality and efficiency of our approach for real-world applications.

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AAAI Conference 2025 Conference Paper

Navigating Towards Fairness with Data Selection

Yixuan Zhang
Zhidong Li
Yang Wang
Fang Chen
Xuhui Fan
Feng Zhou

Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the training process, but these lack flexibility for large-scale datasets. To address this limitation, we introduce a data selection method designed to efficiently and flexibly mitigate label bias, tailored to more practical needs. Our approach utilizes a zero-shot predictor as a proxy model that simulates training on a clean holdout set. This strategy, supported by peer predictions, ensures the fairness of the proxy model and eliminates the need for an additional holdout set, which is a common requirement in previous methods. Without altering the classifier's architecture, our modality-agnostic method effectively selects appropriate training data and has proven efficient and effective in handling label bias and improving fairness across diverse datasets in experimental evaluations.

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AIIM Journal 2025 Journal Article

Toward fair medical advice: Addressing and mitigating bias in large language model-based healthcare applications

Haohui Lu
Ye Lin
Zhidong Li
Man Lung Yiu
Yu Gao
Shahadat Uddin

Large Language Models (LLMs) are increasingly deployed in web-based medical advice applications, offering scalable and accessible healthcare solutions. However, their outputs often reflect demographic biases, raising concerns about fairness and equity for vulnerable populations. In this work, we propose FairMed, a framework designed to mitigate biases in LLM-generated medical advice through fine-tuning and prompt engineering strategies. We evaluate FairMed using language-based and content-level metrics across demographic groups on publicly available (MedQA), synthetic (Synthea), and private (CBHS) datasets. Experimental results demonstrate consistent improvements over Llama3 - Med42, as well as over the zero-shot prompting baseline. For instance, in sentiment analysis for gender groups using MedQA, FairMed with Descriptive Prompting reduces the Statistical Parity Difference (SPD) from 0. 0902 to 0. 0658, improves the Disparate Impact Ratio from 1. 1916 to 1. 1566, and decreases the Kullback–Leibler Divergence from 0. 0045 to 0. 0024. Similarly, in directive language evaluation for gender groups using Synthea, SPD improves from 0. 1056 to nearly zero, achieving near-perfect parity. On the CBHS dataset, FairMed with Descriptive Prompting increases Diagnostic Recommendation Divergence (DRD) for race groups from 0. 9530 to 0. 9848, indicating improved group-specific tailoring, while reducing the Action Disparity Index (ADI) from 0. 0857 to 0. 0469 and Referral Frequency Parity (RFP) from 0. 0791 to 0. 0511, reflecting enhanced fairness. These findings highlight FairMed’s effectiveness in addressing demographic disparities and promoting equitable healthcare guidance through web technologies. This framework contributes to building trustworthy and inclusive systems for delivering medical advice by ensuring fairness in sensitive applications.

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ECAI Conference 2024 Conference Paper

TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes

Zizhuo Meng
Boyu Li 0003
Xuhui Fan 0001
Zhidong Li
Yang Wang 0002
Fang Chen 0001
Feng Zhou 0011

The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs. Our code is available at https: //github. com/waystogetthere/TransFeat. git.

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JMLR Journal 2020 Journal Article

Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables

Feng Zhou
Zhidong Li
Xuhui Fan
Yang Wang
Arcot Sowmya
Fang Chen

The expressive ability of classic Hawkes processes is limited due to the parametric assumption on the baseline intensity and triggering kernel. Therefore, it is desirable to perform inference in a data-driven, nonparametric approach. Many recent works have proposed nonparametric Hawkes process models based on Gaussian processes (GP). However, the likelihood is non-conjugate to the prior resulting in a complicated and time-consuming inference procedure. To address the problem, we present the sigmoid Gaussian Hawkes process model in this paper: the baseline intensity and triggering kernel are both modeled as the sigmoid transformation of random trajectories drawn from a GP. By introducing auxiliary latent random variables (branching structure, P\'{o}lya-Gamma random variables and latent marked Poisson processes), the likelihood is converted to two decoupled components with a Gaussian form which allows for an efficient conjugate analytical inference. Using the augmented likelihood, we derive an efficient Gibbs sampling algorithm to sample from the posterior; an efficient expectation-maximization (EM) algorithm to obtain the maximum a posteriori (MAP) estimate and furthermore an efficient mean-field variational inference algorithm to approximate the posterior. To further accelerate the inference, a sparse GP approximation is introduced to reduce complexity. We demonstrate the performance of our three algorithms on both simulated and real data. The experiments show that our proposed inference algorithms can recover well the underlying prompting characteristics efficiently. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

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