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Xiaohui Tao

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13 papers

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EAAI Journal 2026 Journal Article

Balancing privacy and performance: An empirical study of machine unlearning in deep learning models

Tazeem Ahmad
Xiaohui Tao
Jianming Yong
Thanveer Shaik
Haoran Xie
Yuefeng Li
U. Rajendra Acharya

In the field of Artificial Intelligence (AI), the data used to train models may contain private information that could potentially be exposed in the model’s output. Machine Unlearning (MU) has emerged as a promising solution for removing private or obsolete data from trained models, along with their influence, thereby enforcing the “right to be forgotten” under the General Data Protection Regulation (GDPR). However, achieving a balance between privacy guarantee and model performance remains a fundamental challenge. This paper contributes to the field of AI by presenting an empirical evaluation of key families, i. e. , data deletion, data perturbation, and model update of MU for privacy preservation, focusing on their impact on both classification accuracy and privacy in deep learning (DL) models. The study assesses changes in the classification performance of the convolutional neural network (CNN) architecture and the long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural network (RNN) architectures when used with data deletion, data perturbation, and model update families of MU. This study also assesses these architectures’ susceptibility to membership inference attacks (MIA) before and after unlearning on PPG-DaLiA and MHEALTH (Mobile HEALTH) datasets, providing a quantitative measure of privacy leakage. Experimental results show that model update techniques offer more scalable alternatives to data deletion and perturbation, though they introduce varying levels of privacy leakage risk. In doing so, this research highlights the strengths and limitations of current targeted unlearning methods and underscores the need for more efficient and flexible approaches to privacy protection in DL models.

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AAAI Conference 2026 Conference Paper

Exploring Selective Avoidance for Online User Behavior Analysis: A Forest of Thought Explanation

Xiaohua Wu
Lin Li
Kaize Shi
Xiaohui Tao
Jianwei Zhang
Yuefeng Li

The response behaviors observed in online user-generated content (UGC) frequently demonstrate non-linear characteristics, such as conditional branching and selective avoidance. These patterns present additional challenges for ensuring the trustworthiness of Large Language Model (LLMs) reasoning, particularly as their unidirectional, left-to-right inference mechanisms may not adequately capture such complex reasoning dynamics. To address this, we propose a Forest of Thought Explanation (FoTE), a novel prompting that models the selective avoidance in UGC while ensuring explanation consensus through reasoning paths across all decision sub-trees. FoTE firstly generates various reasoning paths through an adaptive CoT prompting. Each generated thought is subsequently evaluated through cooperative game theory to quantify its fair influence. The thoughts with the top-k contribution scores are preserved and randomly sampled to emulate selective avoidance for the next reasoning iteration. Through extensive evaluations across three open-source LLMs and two established social science problems (spanning four benchmark datasets), FoTE demonstrates superior success rates compared to competing prompting strategies. Notably, its performance gains increase with the strength of selective avoidance in social problems. The trustworthiness of our FoTE is enhanced by the incorporation of (1) a solid theoretical foundation and (2) a transparent reasoning path that converges toward consensus.

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EAAI Journal 2026 Journal Article

G-LFFN: A Global-Local Feature Fusion Network Leveraging Transformer-Encoder and Contrastive Learning for Multimodal Sentiment Analysis

Cong Liu
Yong Wang
Jing Yang
Xiaohui Tao
Jiaqi Liu

Due to the varieties of sentiment expressions, multimodal sentiment analysis for social media requires a comprehensive fusion of image and textual information. However, most of the previous studies have only modeled the inter-modal local or global interactions, ignoring inter-modal global and local co-influences, resulting in insufficient fusion of sentiment information. In addition, the introduction of multiple features may generate more sentiment-irrelevant information, thus leading to a weaker sentiment association of the fusion features. To solve the above issues, we propose a global-local feature fusion network model leveraging transformer-encoder and contrastive learning. Firstly, considering inter-modal global and local co-influences, the model extracts global and local features in the image. Secondly, we propose a cross-modal synchronous fusion transformer-encoder and its simplified version to capture inter-modal global and local consistent features, and combine it with soft self-attention to further enhance inter-modal interaction. On this basis, we utilize multiple contrastive learning to enhance the interactions among multiple fusion features and improve the sentiment associations of multimodal fusion features to assist the final sentiment analysis. Extensive experiments on three public multimodal datasets show that our model can adequately capture inter-modal global-local information interactions and effectively improve sentiment associations, thus demonstrating its validity and superiority.

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AAAI Conference 2026 Conference Paper

Modeling Item-Level Dynamic Variability with Residual Diffusion for Bundle Recommendation

Dong Zhang
Lin Li
Ming Li
Amran Bhuiyan
Meng Sun
Xiaohui Tao
Jimmy Huang

Existing solutions for bundle recommendation (BR) have achieved remarkable effectiveness for predicting the user’s preference for prebuilt bundles. However, bundle-item (B-I) affiliation will vary dynamically in real scenarios. For ex ample, a bundle themed as ‘casual outfit’ may add ‘hat’ or remove ‘watch’ due to factors such as seasonal variations, changes in user preferences or inventory adjustments. Our empirical study demonstrates that the performance of main stream BR models may fluctuate or decline under item-level variability. This paper makes the first attempt to address the above problem and proposes Residual Diffusion for Bundle Recommendation (RDiffBR) as a model-agnostic generative framework which can assist a BR model in adapting this sce nario. During the initial training of the BR model, RDiffBR employs a residual diffusion model to process the item-level bundle embeddings which are generated by the BR model to represent bundle theme via a forward-reverse process. In the inference stage, RDiffBR reverses item-level bundle em beddings obtained by the well-trained bundle model under B-I variability scenarios to generate the effective item-level bundle embeddings. In particular, the residual connection in our residual approximator significantly enhances BR mod els’ ability to generate high-quality item-level bundle embed dings. Experiments on six BRmodelsandfourpublicdatasets from different domains show that RDiffBR improves the per formance of Recall and NDCG of backbone BR models by up to 23%, while only increases training time about 4%.

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IJCAI Conference 2025 Conference Paper

A Survey on Multi-View Knowledge Graph: Generation, Fusion, Applications and Future Directions

Zihan Yang
Xiaohui Tao
Taotao Cai
Yifu Tang
Haoran Xie
Lin Li
Jianxin Li
Qing Li

Knowledge Graphs (KGs) have revolutionized structured knowledge representation, yet their capacity to model real-world complexity and heterogeneity remains fundamentally constrained. The emerging paradigm of Multi-View Knowledge Graphs (MVKGs) addresses this gap through multi-view learning, but existing research lacks systematic integration. This survey provides the first systematic consolidation of MVKG methodologies, with four pivotal contributions: 1) The first unified taxonomy of view generation paradigms that rigorously categorizes view into four types: structure, semantic, representation, and knowledge & modality; 2) A novel methodological typology for view fusion that systematically classifies techniques by fusion targets (feature, decision, and hybrid); 3) Task-centric application mapping that bridges theoretical MVKG constructs to node/link/graph-level downstream tasks; 4) A forward-looking roadmap identifying underexplored challenges. By unifying fragmented methodologies and formalizing MVKG design principles, this survey serves as a roadmap for advancing KG versatility in complex AI-driven scenarios. In doing so, it paves the way for more efficient knowledge integration, enhanced decision-making, and cross-domain learning in real-world applications.

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IS Journal 2025 Journal Article

Exploring ChatGPT-Based Augmentation Strategies for Contrastive Aspect-Based Sentiment Analysis

Lingling Xu
Haoran Xie
S. Joe Qin
Fu Lee Wang
Xiaohui Tao

Aspect-based sentiment analysis (ABSA) involves identifying sentiment toward specific aspect terms in a sentence and allows us to uncover people’s nuanced perspectives and attitudes on particular aspects of a product, service, or topic. However, the scarcity of labeled data poses a significant challenge to training high-quality models. To address this issue, we explore the potential of data augmentation using ChatGPT, a well-performing large language model, to enhance the sentiment classification performance toward aspect terms. Specifically, we explore three data augmentation strategies based on ChatGPT: context-focused, aspect-focused, and context–aspect data augmentation techniques. Context-focused data augmentation focuses on changing the word expression of context words in the sentence while keeping aspect terms unchanged. In contrast, aspect-focused data augmentation aims to change aspect terms but keep context words unchanged. Context–aspect data augmentation integrates these two data augmentations to generate augmented samples. Furthermore, we incorporate contrastive learning into the ABSA tasks to improve performance. Extensive experiments show that all three data augmentation techniques lead to performance improvements, with the context–aspect data augmentation strategy performing best and surpassing the performance of the baseline models.

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NeurIPS Conference 2025 Conference Paper

Factor Decorrelation Enhanced Data Removal from Deep Predictive Models

Wenhao Yang
Lin Li
Xiaohui Tao
Kaize Shi

The imperative of user privacy protection and regulatory compliance necessitates sensitive data removal in model training, yet this process often induces distributional shifts that undermine model performance-particularly in out-of-distribution (OOD) scenarios. We propose a novel data removal approach that enhances deep predictive models through factor decorrelation and loss perturbation. Our approach introduces: (1) a discriminative-preserving factor decorrelation module employing dynamic adaptive weight adjustment and iterative representation updating to reduce feature redundancy and minimize inter-feature correlations. (2) a smoothed data removal mechanism with loss perturbation that creates information-theoretic safeguards against data leakage during removal operations. Extensive experiments on five benchmark datasets show that our approach outperforms other baselines and consistently achieves high predictive accuracy and robustness even under significant distribution shifts. The results highlight its superior efficiency and adaptability in both in-distribution and out-of-distribution scenarios.

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IS Journal 2025 Journal Article

Leveraging ChatGPT-Based Augmentation and Contrastive Learning for Chinese Massive Open Online Course Sentiment Analysis

Xieling Chen
Haoran Xie
S. Joe Qin
Lingling Xu
Xiaohui Tao
Fu Lee Wang

This study addresses the unique challenges of sentiment analysis in Chinese massive open online course (MOOC) reviews, where pedagogically embedded language, intra-sentence sentiment shifts, and class imbalance complicate classification tasks. To tackle these domain-specific issues, we integrated ChatGPT-based data augmentation with contrastive learning within a Bidirectional Encoder Representations from Transformers (BERT)–Chinese framework. We evaluated ChatGPT-based augmentation (GPTaug), similar word replacement, and random word deletion under a dual-loss setup that combines supervised cross-entropy and InfoNCE (information noise-constrastive estimation) contrastive learning, focusing on how they enhance model performance across sentiment categories. The results revealed that the integration of contrastive learning with data augmentation strategies substantially improved sentiment classification in Chinese MOOC reviews. Especially, GPTaug demonstrated robust and balanced performance across polarity categories, particularly enhancing the detection of underrepresented neutral sentiments. These findings suggest that generative augmentation, when aligned with contrastive objectives, mitigates data sparsity and semantic ambiguity in educational sentiment analysis.

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TIST Journal 2024 Journal Article

Boosting Healthiness Exposure in Category-Constrained Meal Recommendation Using Nutritional Standards

Ming Li
Lin Li
Xiaohui Tao
Zhongwei Xie
Qing Xie
Jingling Yuan

Food computing, a newly emerging topic, is closely linked to human life through computational methodologies. Meal recommendation, a food-related study about human health, aims to provide users a meal with courses constrained from specific categories (e.g., appetizers, main dishes) that can be enjoyed as a service. Historical interaction data, important user information, is often used by existing models to learn user preferences. However, if a user’s preferences favor less healthy meals, the model will follow that preference and make similar recommendations, potentially negatively impacting the user’s long-term health. This emphasizes the necessity for health-oriented and responsible meal recommendation systems. In this article, we propose a healthiness-aware and category-wise meal recommendation model called CateRec, which boosts healthiness exposure by using nutritional standards as knowledge to guide the model training. Two fundamental questions are raised and answered: (1) How can the healthiness of meals be evaluated? Two well-known nutritional standards from the World Health Organization and the United Kingdom Food Standards Agency are used to calculate the healthiness score of the meal. (2) How can the model training be guided in a health-oriented manner? We construct category-wise personalization partial rankings and category-wise healthiness partial rankings, and theoretically analyze that they meet the necessary properties and assumptions required to be trained by the maximum posterior estimator under Bayesian probability. The data analysis confirms the existence of user preferences leaning towards less healthy meals in two public datasets. A comprehensive experiment demonstrates that our CateRec effectively boosts healthiness exposure in terms of mean healthiness score and ranking exposure while being comparable to the state-of-the-art model in terms of recommendation accuracy.

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TIST Journal 2024 Journal Article

Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning

Simi Job
Xiaohui Tao
Lin Li
Haoran Xie
Taotao Cai
Jianming Yong
Qing Li

Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment procedures into critical care decision-making can be challenging due to the heterogeneous nature of medical data. Advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL) techniques, enables the development of personalized treatment strategies for severe illnesses by using a learning agent to recommend optimal policies. In this study, we propose a Deep Reinforcement Learning (DRL) model with a tailored reward function and an LSTM-GRU-derived state representation to formulate optimal treatment policies for vasopressor administration in stabilizing patient physiological states in critical care settings. Using an ICU dataset and the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we focus on patients with Acute Respiratory Distress Syndrome (ARDS) that has led to Sepsis, to derive optimal policies that can prioritize patient recovery over patient survival. Both the DDQN ( RepDRL-DDQN ) and Dueling DDQN ( RepDRL-DDDQN ) versions of the DRL model surpass the baseline performance, with the proposed model’s learning agent achieving an optimal learning process across our performance measuring schemes. The robust state representation served as the foundation for enhancing the model’s performance, ultimately providing an optimal treatment policy focused on rapid patient recovery.

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AIIM Journal 2023 Journal Article

Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990–2022)

Joshua Sheehy
Hamish Rutledge
U. Rajendra Acharya
Hui Wen Loh
Raj Gururajan
Xiaohui Tao
Xujuan Zhou
Yuefeng Li

Objective Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs. Methods Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly. Results Random forest (22. 30 %) and support vector machine (21. 58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48. 20 %, 51. 08 % and 17. 27 % of studies, respectively, with some studies using multiple modalities. 21. 58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes. Conclusion There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.

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AAAI Conference 2023 Short Paper

Long Legal Article Question Answering via Cascaded Key Segment Learning (Student Abstract)

Shugui Xie
Lin Li
Jingling Yuan
Qing Xie
Xiaohui Tao

Current sentence-level evidence extraction based methods may lose the discourse coherence of legal articles since they tend to make the extracted sentences scattered over the article. To solve the problem, this paper proposes a Cascaded Answer-guided key segment learning framework for long Legal article Question Answering, namely CALQA. The framework consists of three cascaded modules: Sifter, Reader, and Responder. The Sifter transfers a long legal article into several segments and works in an answer-guided way by automatically sifting out key fact segments in a coarse-to-fine approach through multiple iterations. The Reader utilizes a set of attention mechanisms to obtain semantic representations of the question and key fact segments. Finally, considering it a multi-label classification task the Responder predicts final answers in a cascaded manner. CALQA outperforms state-of-the-art methods in CAIL 2021 Law dataset.

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IJCAI Conference 2022 Conference Paper

Towards the Quantitative Interpretability Analysis of Citizens Happiness Prediction

Lin Li
Xiaohua Wu
Miao Kong
Dong Zhou
Xiaohui Tao

Evaluating the high-effect factors of citizens' happiness is beneficial to a wide range of policy-making for economics and politics in most countries. Benefiting from the high-efficiency of regression models, previous efforts by sociology scholars have analyzed the effect of happiness factors with high interpretability. However, restricted to their research concerns, they are specifically interested in some subset of factors modeled as linear functions. Recently, deep learning shows promising prediction accuracy while addressing challenges in interpretability. To this end, we introduce Shapley value that is inherent in solid theory for factor contribution interpretability to work with deep learning models by taking into account interactions between multiple factors. The proposed solution computes the Shapley value of a factor, i. e. , its average contribution to the prediction in different coalitions based on coalitional game theory. Aiming to evaluate the interpretability quality of our solution, experiments are conducted on a Chinese General Social Survey (CGSS) questionnaire dataset. Through systematic reviews, the experimental results of Shapley value are highly consistent with academic studies in social science, which implies our solution for citizens' happiness prediction has 2-fold implications, theoretically and practically.

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