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Lizhen Cui

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39 papers

2 author rows

AAAI Conference 2026 Conference Paper

Counterfactual Fairness with Imperfect Causal Graphs

Cong Su
Qiaoyu Tan
Carlotta Domeniconi
Lizhen Cui
Jun Wang
Guoxian Yu

Fairness-aware machine learning aims to build predictive models that comply with fairness requirements, particularly concerning sensitive attributes such as race, gender, and age. Among causality-based fairness notions, counterfactual fairness is widely adopted for its individual-level guarantees, requiring that an individual’s predicted outcome remains unchanged in a counterfactual world where its sensitive attribute is altered. However, existing methods critically assume that the true causal graph is fully known, which is rarely the case in practice. Moreover, counterfactual fairness suffers from inherent identifiability limitations, as counterfactual quantities cannot always be uniquely estimated from observational data, especially under incomplete causal knowledge. To address these challenges, we propose a principled framework (CF-ICG) for counterfactual fairness under imperfectly known causal graphs, e.g., Completed Partially Directed Acyclic Graphs (CPDAGs). We first introduce a criterion to determine the identifiability, and bound the counterfactual quantities under CPDAGs. Building upon this, we develop an efficient local algorithm that avoids the exhaustive enumeration of all DAGs, ensuring robustness against worst-case fairness violations. Experimental results on synthetic and real-world datasets demonstrate the practical effectiveness and theoretical soundness of CF-ICG.

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JBHI Journal 2026 Journal Article

Generation and Selection: A Self-Iterative Two-Stage Data Augmentation Method for Automated ECG Classification

Chaoying Jiang
Yujing Xin
Ning Liu
Yonghui Xu
Lei Liu
Lizhen Cui
Jianyong Wang

Automated electrocardiogram (ECG) classification tasks play a crucial role in clinical but face challenges due to the scarcity of accessible and well-labeled data. ECG data augmentation is an effective way to address these challenges, either by altering the characteristics of real ECG data or using statistical and generative models to generate labeled data. However, the generated data often suffer from noise in both the data and label, which can reduce the performance of classification models. To address this, we propose a novel self-iterative two-stage data augmentation method for automated ECG classification, called SiTs-ECG. In the generation stage, an unconditional diffusion model, guided by a Transformer encoder, is trained to capture the complex characteristics of long-term ECG signals, generating high-quality ECG-like samples. In the selection stage, the generated samples are assigned pseudo-labels by a well-trained base classification model, and those generated samples for which the model can confidently predict the pseudo-labels are selected. We then integrate these stages into a self-iterative training process to continually improve the performance of base classification model. Extensive experiments on three real-world datasets demonstrate the effectiveness of our method. Notably, on the Apnea-ECG dataset, using ECG-Transformer as the downstream classification model, Precision, Recall, F1, and Accuracy are improved by 7. 9, 9. 1, 9. 2, and 7. 3 percentage points, respectively. Furthermore, our method is versatile and compatible with various generative and downstream classification models, showing promising applications in automated ECG classification in the clinical field.

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AAAI Conference 2025 Conference Paper

Debate on Graph: A Flexible and Reliable Reasoning Framework for Large Language Models

Jie Ma
Zhitao Gao
Qi Chai
Wangchun Sun
Pinghui Wang
Hongbin Pei
Jing Tao
Lingyun Song

Large Language Models (LLMs) may suffer from hallucinations in real-world applications due to the lack of relevant knowledge. In contrast, knowledge graphs encompass extensive, multi-relational structures that store a vast array of symbolic facts. Consequently, integrating LLMs with knowledge graphs has been extensively explored, with Knowledge Graph Question Answering (KGQA) serving as a critical touchstone for the integration. This task requires LLMs to answer natural language questions by retrieving relevant triples from knowledge graphs. However, existing methods face two significant challenges: *excessively long reasoning paths distracting from the answer generation*, and *false-positive relations hindering the path refinement*. In this paper, we propose an iterative interactive KGQA framework that leverages the interactive learning capabilities of LLMs to perform reasoning and Debating over Graphs (DoG). Specifically, DoG employs a subgraph-focusing mechanism, allowing LLMs to perform answer trying after each reasoning step, thereby mitigating the impact of lengthy reasoning paths. On the other hand, DoG utilizes a multi-role debate team to gradually simplify complex questions, reducing the influence of false-positive relations. This debate mechanism ensures the reliability of the reasoning process. Experimental results on five public datasets demonstrate the effectiveness and superiority of our architecture. Notably, DoG outperforms the state-of-the-art method ToG by 23.7% and 9.1% in accuracy on WebQuestions and GrailQA, respectively. Furthermore, the integration experiments with various LLMs on the mentioned datasets highlight the flexibility of DoG.

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ICML Conference 2025 Conference Paper

Efficient Heterogeneity-Aware Federated Active Data Selection

Ying-Peng Tang
Chao Ren 0006
Xiaoli Tang 0001
Sheng-Jun Huang
Lizhen Cui
Han Yu 0001

Federated Active Learning (FAL) aims to learn an effective global model, while minimizing label queries. Owing to privacy requirements, it is challenging to design effective active data selection schemes due to the lack of cross-client query information. In this paper, we bridge this important gap by proposing the Federated Active data selection by LEverage score sampling (FALE) method. It is designed for regression tasks in the presence of non-i. i. d. client data to enable the server to select data globally in a privacy-preserving manner. Based on FedSVD, FALE aims to estimate the utility of unlabeled data and perform data selection via leverage score sampling. Besides, a secure model learning framework is designed for federated regression tasks to exploit supervision. FALE can operate without requiring an initial labeled set and select the instances in a single pass, significantly reducing communication overhead. Theoretical analyze establishes the query complexity for FALE to achieve constant factor approximation and relative error approximation. Extensive experiments on 11 benchmark datasets demonstrate significant improvements of FALE over existing state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

Exploring Intrinsic Alignments Within Text Corpus

Zi Liang
Pinghui Wang
Ruofei Zhang
Haibo Hu
Shuo Zhang
Qingqing Ye
Nuo Xu
Yaxin Xiao

Recent years have witnessed rapid advancements in the safety alignments of large language models (LLMs). Methods such as supervised instruction fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) have thus emerged as vital components in constructing LLMs. While these methods achieve robust and fine-grained alignment to human values, their practical application is still hindered by high annotation costs and incomplete human alignments. Besides, the intrinsic human values within training corpora have not been fully exploited. To address these issues, we propose ISAAC (Intrinsically Supervised Alignments by Assessing Corpus), a primary and coarse-grained safety alignment strategy for LLMs. ISAAC only relies on a prior assumption about the text corpus, and does not require preferences in RLHF or human responses selection in SFT. Specifically, it assumes a long-tail distribution of text corpus and employs a specialized sampling strategy to automatically sample high-quality responses. Theoretically, we prove that this strategy can improve the safety of LLMs under our assumptions. Empirically, our evaluations on mainstream LLMs show that ISAAC achieves a safety score comparable to current SFT solutions. Moreover, we conduct experiments on ISAAC for some RLHF-based LLMs, where we find that ISAAC can even improve the safety of these models under specific safety domains. These findings demonstrate that ISAAC can provide preliminary alignment to LLMs, thereby reducing the construction costs of existing human-feedback-based methods.

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AAAI Conference 2025 Conference Paper

Federated Causally Invariant Feature Learning

Xianjie Guo
Kui Yu
Lizhen Cui
Han Yu
Xiaoxiao Li

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://github.com/Xianjie-Guo/FedCIFL.

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JBHI Journal 2025 Journal Article

Finer-Grained Dynamic Functional Graph Structure Learning for EEG Sequence Modeling

Xiaofang Sun
Hangwei Qian
Yonghui Xu
Yongqing Zheng
Lizhen Cui

Electroencephalography (EEG) serves as a critical neuroimaging observation instrument to understand brain dynamics, yet accurately modeling the dynamic functional connectivity inherent in EEG signals remains an open challenge. The existing statistically based coarse-grained associations based on fixed physical distances or static graph structures cannot reflect the spatio-temporal specificity of dynamic functional interactions between brain regions. The secondary computation based on threshold or attention mechanisms to eliminate redundant connections cannot dynamically obtain global structural changes at a fine-grained level, ignoring the rapid dynamic reorganization of brain region functions and lacking a fast response to input correlations. More recently, state space models have been shown to excel at processing long data sequences. However, directly applying such models to EEG data is far from satisfactory due to the lack of functional connectivity modeling between brain regions. In this paper, we propose the Dynamic Functional Graph Structure Learning framework (DFGSL) to capture the dynamic functional connectivity with state space models at a finer-grained level. The proposed DFGSL first constructs dynamic similarity probability maps to reveal information exchange between brain regions. Then, we simulate the entire dynamic evolution of dynamic functional connectivity at a finer-grained level through a selective state space model. By calculating the dynamic similarity probability between selected states, we obtain a compact state representation to describe the context of the dynamic evolution of brain state and reveal potential neural mechanisms. Empirical experiments on three benchmark datasets with different populations, electrode numbers, and brain states show that the proposed DFGSL consistently outperforms state-of-the-art methods, demonstrating strong functional modeling capabilities.

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IJCAI Conference 2025 Conference Paper

Multi-Agent Communication with Information Preserving Graph Contrastive Learning

Wei Du
Shifei Ding
Wei Guo
Yuqing Sun
Guoxian Yu
Lizhen Cui

Recent research in cooperative Multi-Agent Reinforcement Learning (MARL) has shown significant interest in utilizing Graph Neural Networks (GNNs) for communication learning due to their strong ability to process feature and topological information of agents into message representations for downstream action selection and coordination. However, GNNs generally assume network homogeneity that nodes of the same class tend to be interconnected. In real-world multi-agent systems, such assumptions are often unrealistic, as agents within the same class can be distant from each other. Furthermore, GNN-based MARL methods overlook the crucial role of feature similarity of agents in action coordination, which also restricts their performance. To overcome these limitations, we propose a Multi-Agent communication mechanism with Information preserving graph contrastive Learning (MAIL), which enhances message representation by preserving the comprehensive features of adjacent agents while integrating topological information. Specifically, MAIL considers three distinct graph views: original view, agent feature view, and global topological view. MAIL performs contrastive learning across three views to extract comprehensive information. MAIL effectively learns robust and expressive message representations for downstream tasks. Extensive experiments across various environments demonstrate that MAIL outperforms existing GNN-based MARL methods.

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JBHI Journal 2025 Journal Article

Multi-Modal Disease Prediction With Hierarchical Self-Supervised Learning

Zhe Qu
Taihua Chen
Xin Zhou
Fanglin Zhu
Wei Guo
Yonghui Xu
Yixin Zhang
Lizhen Cui

The proliferation of healthcare data sources, including diverse imaging modalities and biochemical measurements, has created unprecedented opportunities for comprehensive disease prediction. Multi-modal clinical data, encompassing medical imaging reports, biochemical assays, and longitudinal clinical records, provides a rich foundation for developing sophisticated diagnostic models. Graph Neural Networks (GNNs) have emerged as a leading methodological framework, distinguished by their capacity to model complex inter-patient relationships and capture community structures within patient data. Despite their promise, current GNN-based approaches exhibit limitations in handling noisy, low-quality data and often impose overly restrictive graph smoothness constraints. These limitations can obscure patient-specific variations and compromise model robustness. To overcome these challenges, we propose HierSSL ( Hier archical S elf- S upervised L earning), a novel multi-modal disease prediction framework that enhances representational learning through dual-scale self-supervision mechanisms operating at both local and global levels. HierSSL's architecture specifically addresses two critical aspects: 1) the capture of local inter-modality dependencies and global community patterns, and 2) the optimization of multi-modal feature integration through an innovative combination of feature consistency constraints and graph contrastive learning. Empirical evaluation across two distinct disease prediction datasets demonstrates that HierSSL achieves statistically significant performance improvements compared to state-of-the-art methods, highlighting its efficacy in robust multi-modal data integration for disease prediction tasks.

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IJCAI Conference 2025 Conference Paper

MutationGuard: A Graph and Temporal-Spatial Neural Method for Detecting Mutation Telecommunication Fraud

Haitao Bai
Pinghui Wang
Ruofei Zhang
Ziyang Zhou
Juxiang Zeng
Yulou Su
Li Xing
Zhou Su

Telecommunication fraud refers to deceptive activities in the field of communication services. This research focuses on a category of fraud identified as ''mutation telecommunication fraud". There is currently a lack of research on mutation telecommunication fraud detection, allowing this type of fraud to persist uncaught. We identify that detecting mutation fraud requires capturing multi-source patterns, including user communication graphs and temporal-spatial Voice of Call (VOC) features. Specifically, we introduce MutationGuard, which leverages Graph Neural Networks (GNN) to capture changes in user communication graphs. For VOC records, we map call start times onto a 3D cylindrical surface, thereby representing each VOC record in spatial coordinates and utilizing proposed LFFE and TCFE modules to capture local fraud behaviors and temporal behavior changes. The proposed neural modeling approach that facilitates multi-source information fusion constitutes a significant advancement in detecting mutation fraud. Experiment results reveal a significant improvement in the AUC score by 1. 52% and the F1 score by 1. 36% on the proposed telecommunication fraud dataset. Particularly, our method shows a significant improvement of 13. 93% in accuracy on mutation fraud data. We also validate the effectiveness of our method on the publicly available Sichuan Telecommunication Fraud dataset.

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TAAS Journal 2024 Journal Article

A Hierarchical Model for Complex Adaptive System: From Adaptive Agent to AI Society

Deyu Zhou
Xiao Xue
Xudong Lu
Yuwei Guo
Peilin Ji
Hongtao Lv
Wei He
Yonghui Xu

As complex adaptive system involves human and social factors (e.g., changing demands, competition and collaboration among agents), accurately modeling the complex features of adaptive agents and AI society is crucial for the effective analysis and governance of complex adaptive systems. However, existing modeling methods struggle to accurately represent these complex features, there is a gap between existing technologies and complex features modeling. In this context, this paper proposes a hierarchical model based on the computational experiments method, which consists of four layers (i.e., L1, L2, L3 and L4) modeling the autonomous, evolutionary, interactive, and emergent features respectively from adaptive agent to AI society. Additionally, taking intelligent transportation system as an example, a computational experiments system is constructed to demonstrate the effectiveness of the proposed model. This model builds a bridge between complex feature modeling and various technologies, thereby offering theoretical support for further research in complex adaptive systems.

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AAAI Conference 2024 Conference Paper

Anchoring Path for Inductive Relation Prediction in Knowledge Graphs

Zhixiang Su
Di Wang
Chunyan Miao
Lizhen Cui

Aiming to accurately predict missing edges representing relations between entities, which are pervasive in real-world Knowledge Graphs (KGs), relation prediction plays a critical role in enhancing the comprehensiveness and utility of KGs. Recent research focuses on path-based methods due to their inductive and explainable properties. However, these methods face a great challenge when lots of reasoning paths do not form Closed Paths (CPs) in the KG. To address this challenge, we propose Anchoring Path Sentence Transformer (APST) by introducing Anchoring Paths (APs) to alleviate the reliance of CPs. Specifically, we develop a search-based description retrieval method to enrich entity descriptions and an assessment mechanism to evaluate the rationality of APs. APST takes both APs and CPs as the inputs of a unified Sentence Transformer architecture, enabling comprehensive predictions and high-quality explanations. We evaluate APST on three public datasets and achieve state-of-the-art (SOTA) performance in 30 of 36 transductive, inductive, and few-shot experimental settings.

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TIST Journal 2024 Journal Article

Explicit Knowledge Graph Reasoning for Conversational Recommendation

Xuhui Ren
Tong Chen
Quoc Viet Hung Nguyen
Lizhen Cui
Zi Huang
Hongzhi Yin

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Recently, there has been a rise of using knowledge graphs (KGs) for CRSs, where the core motivation is to incorporate the abundant side information carried by a KG into both the recommendation and conversation processes. However, existing KG-based CRSs are subject to two defects: (1) there is a semantic gap between the learned representations of utterances and KG entities, hindering the retrieval of relevant KG information; (2) the reasoning over KG is mostly performed with the implicitly learned user interests, overlooking the explicit signals from the entities actually mentioned in the conversation. To address these drawbacks, we propose a new CRS framework, namely, the Knowledge Enhanced Conversational Reasoning (KECR) model. As a user can reflect her/his preferences via both attribute- and item-level expressions, KECR jointly embeds the structured knowledge from two levels in the KG. A mutual information maximization constraint is further proposed for semantic alignment between the embedding spaces of utterances and KG entities. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user’s feedback to clarifying questions. As such, the semantic alignment and explicit KG reasoning can jointly facilitate accurate recommendation and quality dialogue generation. By comparing with strong baselines on two real-world datasets, we demonstrate that KECR obtains state-of-the-art recommendation effectiveness, as well as competitive dialogue generation performance.

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IJCAI Conference 2024 Conference Paper

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

Liping Yi
Han Yu
Zhuan Shi
Gang Wang
Xiaoguang Liu
Lizhen Cui
Xiaoxiao Li

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a. k. a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system heterogeneity. Model-Heterogeneous Personalized FL (MHPFL) has emerged to address this challenge. Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs. To address these limitations, we propose the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks, which splits each client's model into a heterogeneous (structure-different) feature extractor and a homogeneous (structure-same) classification header. It performs local-to-global knowledge transfer via semantic similarity-based header parameter aggregation. In addition, global-to-local knowledge transfer is achieved via an adaptive parameter stabilization strategy which fuses the seen-class parameters of historical local headers with that of the latest global header for each client. FedSSA does not rely on public datasets, while only requiring partial header parameter transmission to save costs. Theoretical analysis proves the convergence of FedSSA. Extensive experiments present that FedSSA achieves up to 3. 62% higher accuracy, 15. 54 times higher communication efficiency, and 15. 52 times higher computational efficiency compared to 7 state-of-the-art MHPFL baselines.

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JBHI Journal 2024 Journal Article

Medicine Package Recommendation via Dual-Level Interaction Aware Heterogeneous Graph

Fanglin Zhu
Xu Zhang
Batuo Zhang
Yonghui Xu
Lizhen Cui

Medicine package recommendation aims to assist doctors in clinical decision-making by recommending appropriate packages of medicines for patients. Current methods model this task as a multi-label classification or sequence generation problem, focusing on learning relationships between individual medicines and other medical entities. However, these approaches uniformly overlook the interactions between medicine packages and other medical entities, potentially resulting in a lack of completeness in recommended medicine packages. Furthermore, medicine commonsense knowledge considered by current methods is notably limited, making it challenging to delve into the decision-making processes of doctors. To solve these problems, we propose DIAGNN, a Dual-level Interaction Aware heterogeneous Graph Neural Network for medicine package recommendation. Specifically, DIAGNN explicitly models interactions of medical entities within electronic health records(EHRs) at two levels, individual medicine and medicine package, leveraging a heterogeneous graph. A dual-level interaction aware graph convolutional network is utilized to capture semantic information in the medical heterogeneous graph. Additionally, we incorporate medication indications into the medical heterogeneous graph as medicine commonsense knowledge. Extensive experimental results on real-world datasets validate the effectiveness of the proposed method.

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IJCAI Conference 2024 Conference Paper

Personalized Federated Learning for Cross-City Traffic Prediction

Yu Zhang
Hua Lu
Ning Liu
Yonghui Xu
Qingzhong Li
Lizhen Cui

Traffic prediction plays an important role in urban computing. However, many cities face data scarcity due to low levels of urban development. Although many approaches transfer knowledge from data-rich cities to data-scarce cities, the centralized training paradigm cannot uphold data privacy. For the sake of inter-city data privacy, Federated Learning has been used, which follows a decentralized training paradigm to enhance traffic knowledge of data-scarce cities. However, spatio-temporal data heterogeneity causes client drift, leading to unsatisfactory traffic prediction performance. In this work, we propose a novel personalized Federated learning method for Cross-city Traffic Prediction (pFedCTP). It learns traffic knowledge from multiple data-rich source cities and transfers the knowledge to a data-scarce target city while preserving inter-city data privacy. In the core of pFedCTP lies a Spatio-Temporal Neural Network (ST-Net) for clients to learn traffic representation. We decouple the ST-Net to learn space-independent traffic patterns to overcome cross-city spatial heterogeneity. Besides, pFedCTP adaptively interpolates the layer-wise global and local parameters to deal with temporal heterogeneity across cities. Extensive experiments on four real-world traffic datasets demonstrate significant advantages of pFedCTP over representative state-of-the-art methods.

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IJCAI Conference 2024 Conference Paper

Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection

Xianjie Guo
Kui Yu
Hao Wang
Lizhen Cui
Han Yu
Xiaoxiao Li

Federated causal discovery (FCD) aims to uncover causal relationships among variables from decentralized data across multiple clients, while preserving data privacy. In practice, the sample quality of each client's local data may vary across different variable spaces, referred to as sample quality heterogeneity. Thus, data from different clients might be suitable for learning different causal relationships among variables. Model aggregated under existing FCD methods requires the entire model parameters from each client, thereby being unable to handle the sample quality heterogeneity issue. In this paper, we propose the Federated Adaptive Causal Discovery (FedACD) method to bridge this gap. During federated model aggregation, it adaptively selects the causal relationships learned under the "good" variable space (i. e. , one with high-quality samples) from each client, while masking those learned under the "bad" variable space (i. e. , one with low-quality samples). This way, each client only needs to send the optimal learning results to the server, achieving accurate FCD. Extensive experiments on various types of datasets demonstrate significant advantages of FedACD over existing methods. The source code is available at https: //github. com/Xianjie-Guo/FedACD.

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AAMAS Conference 2024 Conference Paper

Towards Efficient Auction Design with ROI Constraints

Xinyu Tang
Hongtao Lv
Yingjie Gao
Fan Wu
Lei Liu
Lizhen Cui

Online advertising stands as a significant revenue source of the Internet. Recently, the trend among advertisers tilting towards the use of auto-bidding tools has heralded the emergence of a new model of bidders operating with constraints related to return on investment (ROI). However, most of the current research on ROIconstrained bidders in auction design only focuses on either the ROI constraints or values of bidders being private, while it is more practical to keep them both private in reality. Designing a truthful mechanism for bidders with both private values and ROI constraints introduces complexities because of the characteristics of designing mechanisms with multiple parameters. To remedy this, we divide bidders into binary classes: the traditional utility maximizers (UMs) who can be viewed as having an ROI constraint of 1, and the ROIconstrained bidders (RBs) who share a fixed ROI constraint denoted as 𝛾. This framework retains the essence of multi-parameter mechanism design but transitions this into a more tractable form. Then we introduce a novel auction mechanism, cleverly combining the conventional VCG mechanism and an existing mechanism for public ROI-constrained bidders which is called Cavallo’s mechanism. Our mechanism can achieve an approximation ratio of 3 2 on social welfare. Additionally, we unearth new insights into the limitations posed by ROI constraints. When the ROI constraint 𝛾 exceeds 2, the lower bound of social welfare is 5 4; when it falls below 2, the lower bound becomes 3+𝛾 2+3𝛾−𝛾2.

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JBHI Journal 2023 Journal Article

Heterogeneous Collaborative Learning for Personalized Healthcare Analytics via Messenger Distillation

Guanhua Ye
Tong Chen
Yawen Li
Lizhen Cui
Quoc Viet Hung Nguyen
Hongzhi Yin

The Healthcare Internet-of-Things (IoT) framework aims to provide personalized medical services with edge devices. Due to the inevitable data sparsity on an individual device, cross-device collaboration is introduced to enhance the power of distributed artificial intelligence. Conventional collaborative learning protocols (e. g. , sharing model parameters or gradients) strictly require the homogeneity of all participant models. However, real-life end devices have various hardware configurations (e. g. , compute resources), leading to heterogeneous on-device models with different architectures. Moreover, clients (i. e. , end devices) may participate in the collaborative learning process at different times. In this paper, we propose a S imilarity- Q uality-based M essenger D istillation (SQMD) framework for heterogeneous asynchronous on-device healthcare analytics. By introducing a preloaded reference dataset, SQMD enables all participant devices to distill knowledge from peers via messengers (i. e. , the soft labels of the reference dataset generated by clients) without assuming the same model architecture. Furthermore, the messengers also carry important auxiliary information to calculate the similarity between clients and evaluate the quality of each client model, based on which the central server creates and maintains a dynamic collaboration graph (communication graph) to improve the personalization and reliability of SQMD under asynchronous conditions. Extensive experiments on three real-life datasets show that SQMD achieves superior performance.

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AAAI Conference 2023 Conference Paper

MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series

Qianwen Meng
Hangwei Qian
Yong Liu
Lizhen Cui
Yonghui Xu
Zhiqi Shen

Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absence of expert annotations. However, existing contrastive approaches generally treat each instance independently, which leads to false negative pairs that share the same semantics. To tackle this problem, we propose MHCCL, a Masked Hierarchical Cluster-wise Contrastive Learning model, which exploits semantic information obtained from the hierarchical structure consisting of multiple latent partitions for multivariate time series. Motivated by the observation that fine-grained clustering preserves higher purity while coarse-grained one reflects higher-level semantics, we propose a novel downward masking strategy to filter out fake negatives and supplement positives by incorporating the multi-granularity information from the clustering hierarchy. In addition, a novel upward masking strategy is designed in MHCCL to remove outliers of clusters at each partition to refine prototypes, which helps speed up the hierarchical clustering process and improves the clustering quality. We conduct experimental evaluations on seven widely-used multivariate time series datasets. The results demonstrate the superiority of MHCCL over the state-of-the-art approaches for unsupervised time series representation learning.

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AAAI Conference 2023 Conference Paper

MMTN: Multi-Modal Memory Transformer Network for Image-Report Consistent Medical Report Generation

Yiming Cao
Lizhen Cui
Lei Zhang
Fuqiang Yu
Zhen Li
Yonghui Xu

Automatic medical report generation is an essential task in applying artificial intelligence to the medical domain, which can lighten the workloads of doctors and promote clinical automation. The state-of-the-art approaches employ Transformer-based encoder-decoder architectures to generate reports for medical images. However, they do not fully explore the relationships between multi-modal medical data, and generate inaccurate and inconsistent reports. To address these issues, this paper proposes a Multi-modal Memory Transformer Network (MMTN) to cope with multi-modal medical data for generating image-report consistent medical reports. On the one hand, MMTN reduces the occurrence of image-report inconsistencies by designing a unique encoder to associate and memorize the relationship between medical images and medical terminologies. On the other hand, MMTN utilizes the cross-modal complementarity of the medical vision and language for the word prediction, which further enhances the accuracy of generating medical reports. Extensive experiments on three real datasets show that MMTN achieves significant effectiveness over state-of-the-art approaches on both automatic metrics and human evaluation.

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AAAI Conference 2023 Conference Paper

Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer

Zhixiang Su
Di Wang
Chunyan Miao
Lizhen Cui

Recent studies on knowledge graphs (KGs) show that path-based methods empowered by pre-trained language models perform well in the provision of inductive and explainable relation predictions. In this paper, we introduce the concepts of relation path coverage and relation path confidence to filter out unreliable paths prior to model training to elevate the model performance. Moreover, we propose Knowledge Reasoning Sentence Transformer (KRST) to predict inductive relations in KGs. KRST is designed to encode the extracted reliable paths in KGs, allowing us to properly cluster paths and provide multi-aspect explanations. We conduct extensive experiments on three real-world datasets. The experimental results show that compared to SOTA models, KRST achieves the best performance in most transductive and inductive test cases (4 of 6), and in 11 of 12 few-shot test cases.

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AAAI Conference 2023 Conference Paper

Utility Maximizer or Value Maximizer: Mechanism Design for Mixed Bidders in Online Advertising

Hongtao Lv
Zhilin Zhang
Zhenzhe Zheng
Jinghan Liu
Chuan Yu
Lei Liu
Lizhen Cui
Fan Wu

Digital advertising constitutes one of the main revenue sources for online platforms. In recent years, some advertisers tend to adopt auto-bidding tools to facilitate advertising performance optimization, making the classical utility maximizer model in auction theory not fit well. Some recent studies proposed a new model, called value maximizer, for auto-bidding advertisers with return-on-investment (ROI) constraints. However, the model of either utility maximizer or value maximizer could only characterize partial advertisers in real-world advertising platforms. In a mixed environment where utility maximizers and value maximizers coexist, the truthful ad auction design would be challenging since bidders could manipulate both their values and affiliated classes, leading to a multi-parameter mechanism design problem. In this work, we address this issue by proposing a payment rule which combines the corresponding ones in classical VCG and GSP mechanisms in a novel way. Based on this payment rule, we propose a truthful auction mechanism with an approximation ratio of 2 on social welfare, which is close to the lower bound of at least 5/4 that we also prove. The designed auction mechanism is a generalization of VCG for utility maximizers and GSP for value maximizers.

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IJCAI Conference 2022 Conference Paper

Enhancing Sequential Recommendation with Graph Contrastive Learning

Yixin Zhang
Yong Liu
Yonghui Xu
Hao Xiong
Chenyi Lei
Wei He
Lizhen Cui
Chunyan Miao

The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appropriate sequence representations. This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR). Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data. Moreover, GCL4SR uses subgraphs of WITG to augment the representation of each interaction sequence. Two auxiliary learning objectives have also been proposed to maximize the consistency between augmented representations induced by the same interaction sequence on WITG, and minimize the difference between the representations augmented by the global context on WITG and the local representation of the original sequence. Extensive experiments on real-world datasets demonstrate that GCL4SR consistently outperforms state-of-the-art sequential recommendation methods.

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TIST Journal 2022 Journal Article

GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

Zelei Liu
Yuanyuan Chen
Han Yu
Yang Liu
Lizhen Cui

Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is important to attract high-quality data owners with appropriate incentive schemes. As an important building block of such incentive schemes, it is essential to fairly evaluate participants’ contribution to the performance of the final FL model without exposing their private data. Shapley Value (SV)–based techniques have been widely adopted to provide a fair evaluation of FL participant contributions. However, existing approaches incur significant computation costs, making them difficult to apply in practice. In this article, we propose the Guided Truncation Gradient Shapley (GTG-Shapley) approach to address this challenge. It reconstructs FL models from gradient updates for SV calculation instead of repeatedly training with different combinations of FL participants. In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required. This is accomplished through extensive experiments under diverse realistic data distribution settings. The results demonstrate that GTG-Shapley can closely approximate actual Shapley values while significantly increasing computational efficiency compared with the state-of-the-art, especially under non-i.i.d. settings.

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TMLR Journal 2022 Journal Article

NoiLin: Improving adversarial training and correcting stereotype of noisy labels

Jingfeng Zhang
Xilie Xu
Bo Han
Tongliang Liu
Lizhen Cui
Gang Niu
Masashi Sugiyama

Adversarial training (AT) formulated as the minimax optimization problem can effectively enhance the model's robustness against adversarial attacks. The existing AT methods mainly focused on manipulating the inner maximization for generating quality adversarial variants or manipulating the outer minimization for designing effective learning objectives. However, empirical results of AT always exhibit the robustness at odds with accuracy and the existence of the cross-over mixture problem, which motivates us to study some label randomness for benefiting the AT. First, we thoroughly investigate noisy labels (NLs) injection into AT's inner maximization and outer minimization, respectively and obtain some observations on when NL injection benefits AT. Second, based on the observations, we propose a simple but effective method---NoiLIn that randomly injects NLs into training data at each training epoch and dynamically increases the NL injection rate once robust overfitting occurs. Empirically, NoiLIn can significantly mitigate the AT's undesirable issue of robust overfitting and even further improve the generalization of the state-of-the-art AT methods. Philosophically, NoiLIn sheds light on a new perspective of learning with NLs: NLs should not always be deemed detrimental, and even in the absence of NLs in the training set, we may consider injecting them deliberately.

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JBHI Journal 2022 Journal Article

TRSA-Net: Task Relation Spatial Co-Attention for Joint Segmentation, Quantification and Uncertainty Estimation on Paired 2D Echocardiography

Xiaoxiao Cui
Yankun Cao
Zhi Liu
Xiaoyu Sui
Jia Mi
Yuezhong Zhang
Lizhen Cui
Shuo Li

Clinical workflow of cardiac assessment on 2D echocardiography requires both accurate segmentation and quantification of the Left Ventricle (LV) from paired apical 4-chamber and 2-chamber. Moreover, uncertainty estimation is significant in clinically understanding the performance of a model. However, current research on 2D echocardiography ignores this vital task while joint segmentation with quantification, hence motivating the need for a unified optimization method. In this paper, we propose a multitask model with Task Relation Spatial co-Attention (referred as TRSA-Net) for joint segmentation, quantification, and uncertainty estimation on paired 2D echo. TRSA-Net achieves multitask joint learning by novelly exploring the spatial correlation between tasks. The task relation spatial co-attention learns the spatial mapping among task-specific features by non-local and co-excitation, which forcibly joints embedded spatial information in the segmentation and quantification. The Boundary-aware Structure Consistency (BSC) and Joint Indices Constraint (JIC) are integrated into the multitask learning optimization objective to guide the learning of segmentation and quantification paths. The BSC creatively promotes structural similarity of predictions, and JIC explores the internal relationship between three quantitative indices. We validate the efficacy of our TRSA-Net on the public CAMUS dataset. Extensive comparison and ablation experiments show that our approach can achieve competitive segmentation performance and highly accurate results on quantification.

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JBHI Journal 2021 Journal Article

Disease Prediction via Graph Neural Networks

Zhenchao Sun
Hongzhi Yin
Hongxu Chen
Tong Chen
Lizhen Cui
Fan Yang

With the increasingly available electronic medical records (EMRs), disease prediction has recently gained immense research attention, where an accurate classifier needs to be trained to map the input prediction signals (e. g. , symptoms, patient demographics, etc.) to the estimated diseases for each patient. However, existing machine learning-based solutions heavily rely on abundant manually labeled EMR training data to ensure satisfactory prediction results, impeding their performance in the existence of rare diseases that are subject to severe data scarcity. For each rare disease, the limited EMR data can hardly offer sufficient information for a model to correctly distinguish its identity from other diseases with similar clinical symptoms. Furthermore, most existing disease prediction approaches are based on the sequential EMRs collected for every patient and are unable to handle new patients without historical EMRs, reducing their real-life practicality. In this paper, we introduce an innovative model based on Graph Neural Networks (GNNs) for disease prediction, which utilizes external knowledge bases to augment the insufficient EMR data, and learns highly representative node embeddings for patients, diseases and symptoms from the medical concept graph and patient record graph respectively constructed from the medical knowledge base and EMRs. By aggregating information from directly connected neighbor nodes, the proposed neural graph encoder can effectively generate embeddings that capture knowledge from both data sources, and is able to inductively infer the embeddings for a new patient based on the symptoms reported in her/his EMRs to allow for accurate prediction on both general diseases and rare diseases. Extensive experiments on a real-world EMR dataset have demonstrated the state-of-the-art performance of our proposed model.

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JBHI Journal 2021 Journal Article

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

Guanhua Ye
Hongzhi Yin
Tong Chen
Hongxu Chen
Lizhen Cui
Xiangliang Zhang

Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i. e. , the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy. In this paper, we advance RR-interval based OSA detection by considering its real-world practicality from energy perspectives. As photoplethysmogram (PPG) pulse sensors are commonly equipped on smart wrist-worn wearable devices (e. g. , smart watches and wristbands), the energy efficiency of the detection model is crucial to fully support an overnight observation on patients. This creates challenges as the PPG sensors are unable to keep collecting continuous signals due to the limited battery capacity on smart wrist-worn devices. Therefore, we propose a novel Frequency Extraction Network (FENet), which can extract features from different frequency bands of the input RR-interval signals and generate continuous detection results with downsampled, discontinuous RR-interval signals. With the help of the one-to-multiple structure, FENet requires only one-third of the operation time of the PPG sensor, thus sharply cutting down the energy consumption and enabling overnight diagnosis. Experimental results on real OSA datasets reveal the state-of-the-art performance of FENet.

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IJCAI Conference 2021 Conference Paper

Few-Shot Partial-Label Learning

Yunfeng Zhao
Guoxian Yu
Lei Liu
Zhongmin Yan
Lizhen Cui
Carlotta Domeniconi

Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient partial-label (PL) samples for training. However, it is more common than not to have just few PL samples at hand when dealing with new tasks. Furthermore, existing few-shot learning algorithms assume precise labels of the support set; as such, irrelevant labels may seriously mislead the meta-learner and thus lead to a compromised performance. How to enable PLL under a few-shot learning setting is an important problem, but not yet well studied. In this paper, we introduce an approach called FsPLL (Few-shot PLL). FsPLL first performs adaptive distance metric learning by an embedding network and rectifying prototypes on the tasks previously encountered. Next, it calculates the prototype of each class of a new task in the embedding network. An unseen example can then be classified via its distance to each prototype. Experimental results on widely-used few-shot datasets demonstrate that our FsPLL can achieve a superior performance than the state-of-the-art methods, and it needs fewer samples for quickly adapting to new tasks.

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AAAI Conference 2021 Conference Paper

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

Xin Xia
Hongzhi Yin
Junliang Yu
Qinyong Wang
Lizhen Cui
Xiangliang Zhang

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the complex high-order information among items. Hypergraph provides a natural way to capture beyond-pairwise relations, while its potential for SBR has remained unexplored. In this paper, we fill this gap by modeling sessionbased data as a hypergraph and then propose a hypergraph convolutional network to improve SBR. Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task. Since the two types of networks both are based on hypergraph, which can be seen as two channels for hypergraph modeling, we name our model DHCN (Dual Channel Hypergraph Convolutional Networks). Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the results validate the effectiveness of hypergraph modeling and selfsupervised task. The implementation of our model is available via https: //github. com/xiaxin1998/DHCN.

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IJCAI Conference 2020 Conference Paper

An AI-empowered Visual Storyline Generator

Chang Liu
Zhao Yong Lim
Han Yu
Zhiqi Shen
Ian Dixon
Zhanning Gao
Pan Wang
Peiran Ren

Video editing is currently a highly skill- and time-intensive process. One of the most important tasks in video editing is to compose the visual storyline. This paper outlines Visual Storyline Generator (VSG), an artificial intelligence (AI)-empowered system that automatically generates visual storylines based on a set of images and video footages provided by the user. It is designed to produce engaging and persuasive promotional videos with an easy-to-use interface. In addition, users can be involved in refining the AI-generated visual storylines. The editing results can be used as training data to further improve the AI algorithms in VSG.

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ICML Conference 2020 Conference Paper

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

Jingfeng Zhang
Xilie Xu
Bo Han 0003
Gang Niu 0001
Lizhen Cui
Masashi Sugiyama
Mohan S. Kankanhalli

Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models. However, it is conservative or even pessimistic so that it sometimes hurts the natural generalization. In this paper, we raise a fundamental question{—}do we have to trade off natural generalization for adversarial robustness? We argue that adversarial training is to employ confident adversarial data for updating the current model. We propose a novel formulation of friendly adversarial training (FAT): rather than employing most adversarial data maximizing the loss, we search for least adversarial data (i. e. , friendly adversarial data) minimizing the loss, among the adversarial data that are confidently misclassified. Our novel formulation is easy to implement by just stopping the most adversarial data searching algorithms such as PGD (projected gradient descent) early, which we call early-stopped PGD. Theoretically, FAT is justified by an upper bound of the adversarial risk. Empirically, early-stopped PGD allows us to answer the earlier question negatively{—}adversarial robustness can indeed be achieved without compromising the natural generalization.

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AAAI Conference 2020 Short Paper

Generating Engaging Promotional Videos for E-commerce Platforms (Student Abstract)

Chang Liu
Han Yu
Yi Dong
Zhiqi Shen
Yingxue Yu
Ian Dixon
Zhanning Gao
Pan Wang

There is an emerging trend for sellers to use videos to promote their products on e-commerce platforms such as Taobao. com. Current video production workﬂow includes the production of visual storyline by human directors. We propose a system to automatically generate visual storyline based on the input set of visual materials (e. g. video clips or still images) and then produce a promotional video. In particular, we propose an algorithm called Shot Composition, Selection and Plotting (ShotCSP), which generates visual storylines leveraging ﬁlm-making principles to improve viewing experience and perceived persuasiveness.

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IJCAI Conference 2020 Conference Paper

Learning Personalized Itemset Mapping for Cross-Domain Recommendation

Yinan Zhang
Yong Liu
Peng Han
Chunyan Miao
Lizhen Cui
Baoli Li
Haihong Tang

Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i. e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.

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AAMAS Conference 2019 Conference Paper

Ethically Aligned Multi-agent Coordination to Enhance Social Welfare

Han Yu
Zhiqi Shen
Lizhen Cui
Yongqing Zheng
Victor R. Lesser

In multi-agent systems (MASs), the complex interactions among self-interested agents can be modelled as stochastic games. Existing decision support approaches dealing with such situations focus on minimizing individual agent’s regret through outperforming other agents in the competitive aspect of the game. Such an approach often results in social welfare not being maximized in the process. In this paper, we propose the regret-minimization-social-welfare-maximization (RMSM) approach. It contains a novel method to quantify how an agent’s sacrifice increases and decreases over time based on queueing system dynamics. In this way, ensuring fairness of distribution of sacrifice among agents and compensating for their previous sacrifices can be translated into maintaining the stability of a queueing system.

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AAAI Conference 2018 Conference Paper

SmartHS: An AI Platform for Improving Government Service Provision

Yongqing Zheng
Han Yu
Lizhen Cui
Chunyan Miao
Cyril Leung
Qiang Yang

Over the years, government service provision in China has been plagued by inefﬁciencies. Previous attempts to address this challenge following a toolbox e-government system model in China were not effective. In this paper, we report on a successful experience in improving government service provision in the domain of social insurance in Shandong Province, China. Through standardization of service work- ﬂows following the Complete Contract Theory (CCT) and the infusion of an artiﬁcial intelligence (AI) engine to maximize the expected quality of service while reducing waiting time, the Smart Human-resource Services (SmartHS) platform transcends organizational boundaries and improves system efﬁciency. Deployments in 3 cities involving 2, 000 participating civil servants and close to 3 million social insurance service cases over a 1 year period demonstrated that SmartHS signiﬁcantly improves user experience with roughly a third of the original front desk staff. This new AI-enhanced mode of operation is useful for informing current policy discussions in many domains of government service provision.

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AAAI Conference 2016 Conference Paper

A Fraud Resilient Medical Insurance Claim System

Yuliang Shi
Chenfei Sun
Qingzhong Li
Lizhen Cui
Han Yu
Chunyan Miao

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difﬁcult challenge for the artiﬁcial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efﬁciently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.

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AAMAS Conference 2016 Conference Paper

A Hybrid Approach for Detecting Fraudulent Medical Insurance Claims (Extended Abstract)

Chenfei Sun
Yuliang Shi
Qingzhong Li
Lizhen Cui
Han Yu
Chunyan Miao

Medical insurance frauds are causing huge losses for public healthcare funds in many countries. Detecting medical insurance frauds is an important and difficult challenge. Because of the complex granularity of data, existing fraud detection approaches tend to be less effective in terms of recalling fraudulent claim behaviours. In this paper, we propose a Hybrid Fraud Detection Approach (HFDA) to address this problem, which is compared with four state-of-the-art approaches using a real-world dataset. Extensive experiment results show that the proposed approach is significantly more effective and efficient.

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