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Yiming Xu

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12 papers

2 author rows

EAAI Journal 2026 Journal Article

Contrast-enhanced heterogeneous multi-view graph for session-based recommendation via subsequence units

Fan Yang
Li Ji
Shuo Zhang
Dunlu Peng
Yiming Xu
Nan Chen

Session-based recommendation aims to capture user’s short-term dynamic preferences based on the dependencies between items within a session, and then predicts the next item that the user is most likely to interact with. Currently, session sequences are typically modeled as single-view structures, which focus on learning the interaction between individual items. However, these methods lack rich contextual information and are difficult to understand the user’s intent from a higher dimensional perspective. To better leverage the associations between contexts, this work proposes a Contrast-enhanced Heterogeneous Multi-view Graph via Subsequence Units (CHMGSU) for session-based recommendation. The sequences are modeled as both single-view and heterogeneous multi-view structures, where the single-view graph is constructed at the level of individual items to learn information transfer between items, while the heterogeneous multi-view graph is built using multiple consecutive items to better grasp the user’s high-dimensional intent. A hybrid readout function extracts the intent of subsequences, and captures relationships with contextual relevance. Next, single-view graph attention networks and heterogeneous multi-view graph neural networks are employed to generate item-level and subsequence-level embeddings. By fusing these two types of information, a session-level embedding with information from different perspectives is formed. The prediction results are optimized using the sample-adaptive loss function and the contrastive control gate. In addition, CHMGSU introduces Tmall, Gowalla, Diginetica and Nowplaying datasets to verify the effectiveness of the model on different types of datasets, and experimental results demonstrate that CHMGSU achieves consistent improvements over state-of-the-art baselines, thereby highlighting the incremental yet meaningful advancements achieved.

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AAAI Conference 2025 Conference Paper

Manhattan Self-Attention Diffusion Residual Networks with Dynamic Bias Rectiﬁcation for BCI-based Few-Shot Learning

Hao Wang
Li Xu
Yuntao Yu
Weiyue Ding
Yiming Xu

The distribution biases and scarcity of samples in multi-source data present significant challenges for few-shot learning (FSL) tasks based on brain-computer interface (BCI). Recent efforts have explored the application of diffusion mechanisms in FSL, typically utilizing labeled data to augment the support set. However, this approach has not effectively utilized unlabeled data nor addressed distribution biases. Inspired by the latest advancements in FSL, we propose the manhattan self-attention diffusion residual networks (MSADiff-Resnet) with dynamic bias rectiﬁcation. This model explicitly adds the manhattan self-attention diffusion layer to resnet, using attention mechanisms and manhattan distance-based decay function to control local diffusion intensity, and adjusts the global diffusion strength through the parameter. This diffusion mechanism bridges labeled and unlabeled data, addressing the limitations associated with sample availability. Additionally, we effectively tackle the distribution biases of multi-source data through inter-class bias rectiﬁcation and dynamic intra-class bias rectiﬁcation. Moreover, this study presents for the first time a universal deep learning framework specifically designed for BCI-based FSL tasks. Extensive experiments on multi-source BCI task datasets have validated the effectiveness of proposed method.

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AAAI Conference 2025 Conference Paper

Out-of-Distribution Generalization on Graphs via Progressive Inference

Yiming Xu
Bin Shi
Zhen Peng
Huixiang Liu
Bo Dong
Chen Chen

The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untenable in practice due to the uncontrollable data generation mechanism. In particular, when the data distribution shows a significant shift, most GNNs would fail to produce reliable predictions and may even make decisions randomly. One of the most promising solutions to improve the model generalization is to pick out causal invariant parts in the input graph. Nonetheless, we observe a significant distribution gap between the causal parts learned by existing methods and the ground-truth, leading to undesirable performance. In response to the above issues, this paper presents GPro, a model that learns graph causal invariance with progressive inference. Specifically, the complicated graph causal invariant learning is decomposed into multiple intermediate inference steps from easy to hard, and the perception of GPro is continuously strengthened through a progressive inference process to extract causal features that are stable to distribution shifts. We also enlarge the training distribution by creating counterfactual samples to enhance the capability of the GPro in capturing the causal invariant parts. Extensive experiments demonstrate that our proposed GPro outperforms the state-of-the-art methods by 4.91% on average. For datasets with more severe distribution shifts, the performance improvement can be up to 6.86%.

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AAAI Conference 2025 Conference Paper

Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective

Yiming Xu
Zhen Peng
Bin Shi
Xu Hua
Bo Dong
Song Wang
Chen Chen

The superiority of graph contrastive learning (GCL) has prompted its application to anomaly detection tasks for more powerful risk warning systems. Unfortunately, existing GCL-based models tend to excessively prioritize overall detection performance while neglecting robustness to structural imbalance, which can be problematic for many real-world networks following power-law degree distributions. Particularly, GCL-based methods may fail to capture tail anomalies (abnormal nodes with low degrees). This raises concerns about the security and robustness of current anomaly detection algorithms and therefore hinders their applicability in a variety of realistic high-risk scenarios. To the best of our knowledge, research on the robustness of graph anomaly detection to structural imbalance has received little scrutiny. To address the above issues, this paper presents a novel GCL-based framework named AD-GCL. It devises the neighbor pruning strategy to filter noisy edges for head nodes and facilitate the detection of genuine tail nodes by aligning from head nodes to forged tail nodes. Moreover, AD-GCL actively explores potential neighbors to enlarge the receptive field of tail nodes through anomaly-guided neighbor completion. We further introduce intra- and inter-view consistency loss of the original and augmentation graph for enhanced representation. The performance evaluation of the whole, head, and tail nodes on multiple datasets validates the comprehensive superiority of the proposed AD-GCL in detecting both head anomalies and tail anomalies.

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ICML Conference 2025 Conference Paper

Toward Efficient Kernel-Based Solvers for Nonlinear PDEs

Zhitong Xu
Da Long
Yiming Xu
Guang Yang
Shandian Zhe
Houman Owhadi

We introduce a novel kernel learning framework toward efficiently solving nonlinear partial differential equations (PDEs). In contrast to the state-of-the-art kernel solver that embeds differential operators within kernels, posing challenges with a large number of collocation points, our approach eliminates these operators from the kernel. We model the solution using a standard kernel interpolation form and differentiate the interpolant to compute the derivatives. Our framework obviates the need for complex Gram matrix construction between solutions and their derivatives, allowing for a straightforward implementation and scalable computation. As an instance, we allocate the collocation points on a grid and adopt a product kernel, which yields a Kronecker product structure in the interpolation. This structure enables us to avoid computing the full Gram matrix, reducing costs and scaling efficiently to a large number of collocation points. We provide a proof of the convergence and rate analysis of our method under appropriate regularity assumptions. In numerical experiments, we demonstrate the advantages of our method in solving several benchmark PDEs.

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EAAI Journal 2024 Journal Article

CADLRA: A multi-charge prediction method based on the Criminal Act-Driven Law Retrieval Augmentation

Jianzhou Feng
Lazhi Zhao
Haonan Qin
Yiming Xu
Ziqi Wang

Legal Artificial Intelligence (Legal AI) has garnered significant attention in both academic and industrial domains in recent years. However, most legal judgment prediction (LJP) methods concentrate on single-charge prediction tasks, ignoring the practical scenario of “one person with multiple charges”. To mitigate this limitation, we propose a multi-charge prediction method based on the Criminal Act-Driven Law Retrieval Augmentation (CADLRA), which utilizes Large Language Models (LLMs) to convert the multi-charge classification task into a dynamic multi-charge generation task, achieving enhanced prediction accuracy. To address knowledge solidification and hallucination in LLMs and align with the legal process of sentencing based on criminal acts and relevant laws, we employ contrastive learning to train a retriever to aid LLMs in charge prediction by referencing prior law articles. Finally, experiments were conducted using the public dataset from the Legal AI Challenge, demonstrating that the CADLRA method has achieved state-of-the-art results in both multi-label classification algorithms and charge prediction.

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AAAI Conference 2024 Conference Paper

Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models

Jiang Zhang
Qiong Wu
Yiming Xu
Cheng Cao
Zheng Du
Konstantinos Psounis

Toxic content detection is crucial for online services to remove inappropriate content that violates community standards. To automate the detection process, prior works have proposed varieties of machine learning (ML) approaches to train Language Models (LMs) for toxic content detection. However, both their accuracy and transferability across datasets are limited. Recently, Large Language Models (LLMs) have shown promise in toxic content detection due to their superior zero-shot and few-shot in-context learning ability as well as broad transferability on ML tasks. However, efficiently designing prompts for LLMs remains challenging. Moreover, the high run-time cost of LLMs may hinder their deployments in production. To address these challenges, in this work, we propose BD-LLM, a novel and efficient approach to bootstrapping and distilling LLMs for toxic content detection. Specifically, we design a novel prompting method named Decision-Tree-of-Thought (DToT) to bootstrap LLMs' detection performance and extract high-quality rationales. DToT can automatically select more fine-grained context to re-prompt LLMs when their responses lack confidence. Additionally, we use the rationales extracted via DToT to fine-tune student LMs. Our experimental results on various datasets demonstrate that DToT can improve the accuracy of LLMs by up to 4.6%. Furthermore, student LMs fine-tuned with rationales extracted via DToT outperform baselines on all datasets with up to 16.9% accuracy improvement, while being more than 60x smaller than conventional LLMs. Finally, we observe that student LMs fine-tuned with rationales exhibit better cross-dataset transferability.

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JBHI Journal 2022 Journal Article

Explainable Dynamic Multimodal Variational Autoencoder for the Prediction of Patients With Suspected Central Precocious Puberty

Yiming Xu
Xiaohong Liu
Liyan Pan
Xiaojian Mao
Huiying Liang
Guangyu Wang
Ting Chen

Central precocious puberty (CPP) is the most common type of precocious puberty and has a significant effect on children. A gonadotropin-releasing hormone (GnRH)-stimulation test is the gold standard for confirming CPP. This test, however, is costly and unpleasant for patients. Therefore, it is critical to developing alternative methods for CPP diagnosis in order to alleviate patient suffering. This study aims to develop an artificial intelligence (AI) diagnostic system for predicting response to the GnRH-stimulation test using data from laboratory tests, electronic health records (EHRs), and pelvic ultrasonography and left-hand radiography reports. The challenges are in integrating these multimodal features into a comprehensive deep learning model in order to achieve an accurate diagnosis while also accounting for the missing or incomplete modalities. To begin, we developed a dynamic multimodal variational autoencoder (DMVAE) that can exploit intrinsic correlations between different modalities to impute features for missing modalities. Next, we combined features from all modalities to predict the outcome of a CPP diagnosis. The experimental results (AUROC 0. 9086) demonstrate that our DMVAE model is superior to standard methods. Additionally, we showed that by setting appropriate operating thresholds, clinicians could diagnose about two-thirds of patients with confidence (1. 0 specificity). Only about one-third of patients require confirmation of their diagnoses using GnRH (or GnRH analog)-stimulation tests. To interpret the results, we implemented an explainer Shapley additive explanation (SHAP) to analyze the local and global feature attributions.

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IJCAI Conference 2021 Conference Paper

k-Nearest Neighbors by Means of Sequence to Sequence Deep Neural Networks and Memory Networks

Yiming Xu
Diego Klabjan

k-Nearest Neighbors is one of the most fundamental but effective classification models. In this paper, we propose two families of models built on a sequence to sequence model and a memory network model to mimic the k-Nearest Neighbors model, which generate a sequence of labels, a sequence of out-of-sample feature vectors and a final label for classification, and thus they could also function as oversamplers. We also propose 'out-of-core' versions of our models which assume that only a small portion of data can be loaded into memory. Computational experiments show that our models on structured datasets outperform k-Nearest Neighbors, a feed-forward neural network, XGBoost, lightGBM, random forest and a memory network, due to the fact that our models must produce additional output and not just the label. On image and text datasets, the performance of our model is close to many state-of-the-art deep models. As an oversampler on imbalanced datasets, the sequence to sequence kNN model often outperforms Synthetic Minority Over-sampling Technique and Adaptive Synthetic Sampling.

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IJCAI Conference 2018 Conference Paper

Hierarchical Electricity Time Series Forecasting for Integrating Consumption Patterns Analysis and Aggregation Consistency

Yue Pang
Bo Yao
Xiangdong Zhou
Yong Zhang
Yiming Xu
Zijing Tan

Electricity demand forecasting is a very important problem for energy supply and environmental protection. It can be formalized as a hierarchical time series forecasting problem with the aggregation constraints according to the geographical hierarchy, since the sum of the prediction results of the disaggregated time series should be equal to the prediction results of the aggregated ones. However in most previous work, the aggregation consistency is ensured at the loss of forecast accuracy. In this paper, we propose a novel clustering-based hierarchical electricity time series forecasting approach. Instead of dealing with the geographical hierarchy directly, we explore electricity consumption patterns by clustering analysis and build a new consumption pattern based time series hierarchy. We then present a novel hierarchical forecasting method with consumption hierarchical aggregation constraints to improve the electricity demand predictions of the bottom level, followed by a ``bottom-up" method to obtain forecasts of the geographical higher levels. Especially, we observe that in our consumption pattern based hierarchy the reconciliation error of the bottom level time series is ``correlated" to its membership degree of the corresponding cluster (consumption pattern), and hence apply this correlations as the regularization term in our forecasting objective function. Extensive experiments on real-life datasets verify that our approach achieves the best prediction accuracy, compared with the state-of-the-art methods.

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IROS Conference 2014 Conference Paper

A fish-like locomotion model in an ideal fluid with lateral-line-inspired background flow estimation

Yiming Xu
Kamran Mohseni

Considerable evidence suggests that the lateral line system serves an important role in fish-like locomotion by providing hydrodynamic information about the surrounding fluid. In this paper, a fish-like locomotion model is developed in an ideal fluid with a background flow estimation algorithm inspired by the lateral line system. Specifically, the fish model is geometrically formulated with the Joukowski transformation and is dynamically defined as a deformable solid body. Describing the fluid-body interactions is a hydrodynamic model that incorporates the potential flow theory with a shedding mechanism of discrete vortices. With the hydrodynamic information on the boundary of the fish-like profile, the background flow field can be estimated with a modified vortex panel method. Base on the estimation, the control decision can potentially be adjusted accordingly, which may improve the swimming performance. Furthermore, in the profile model, body segments are defined such that the internal forces can be easily obtained for quantitative efficiency evaluation.

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IROS Conference 2013 Conference Paper

Fish lateral line inspired hydrodynamic feedforward control for autonomous underwater vehicles

Yiming Xu
Kamran Mohseni

Studies have shown that the lateral line found in most fish and some other aquatic organisms is capable of providing hydrodynamic information of the surrounding fluid, which may facilitate many behavioral decisions. Previous work by the group introduced a lateral line inspired feedforward design for underwater vehicle control. The system utilizes pressure sensor arrays to estimate the hydrodynamic force acting on the vehicle such that the additional information will simplify the modeling process and improve the maneuvering accuracy for the control tasks in underwater exploration and environmental monitoring. In this paper, the feedforward control design is presented and tested in simulation for trajectory tracking and path following after expressing the force estimation algorithm in the three-dimensional domain. Pressure measurements at multiple locations on the vehicle surface form a least squares approximation of the pressure distribution. Hydrodynamic forces acting on the vehicle are then estimated and passed to the controller for improved performance. Preliminary experimental tests are conducted to vindicate the proposed algorithm.

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