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Jie Xu

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55 papers
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55

YNIMG Journal 2026 Journal Article

Cerebro-cerebellar structure-function coupling’s role in motor recovery after infarction

  • Jing Liu
  • Yi Shan
  • Bi-Xiao Cui
  • Zhen-Ming Wang
  • Shao-Zhen Yan
  • Jie Xu
  • Lin-Lin Ye
  • Lei Cao

OBJECTIVE: To investigate the pathway-specific structure-function coupling induced by focal subcortical infarction and its influence on clinical symptoms. METHODS: In this prospective study, 50 patients with unilateral subcortical infarction and motor impairment and 50 matched controls underwent resting state fMRI, DTI, and Fugl-Meyer-Assessment lower-extremity (FMA-LE) at 7-14- and 30-days post-infarction. To analyze the pathway-specific structure-function coupling, we evaluated the association between structural integrity of the corticospinal tract (CST), dentate thalamocortical tract (DTCT), cortico-pontocerebellar tract (CPCT), and dorsal spinocerebellar tract (DSCT) and functional connectivity (FC) of corresponding subregions. Moderation analysis assesses whether the structure-function coupling pathway moderates FMA-LE. RESULTS: At baseline, patients exhibited significantly lower structural integrity of DTCT, DSCT, and CST than controls. We found structure-function couplings in the three motor pathways of the cerebro-cerebellar circuit: (1) contralesional thalamus to ipsilesional cerebellum-crus_2 with dentate thalamocortical tract (DTCT), (2) contralesional thalamus to cerebellum vermis_10 with dorsal spinocerebellar tract (DSCT), (3) ipsilesional precentral gyrus to frontal medial gyrus with CST. The baseline DSCT structural integrity specificity modulates the relationship between FC and FMA-LE over 30 days. CONCLUSIONS: We observed that cerebro-cerebellar circuit structure-function coupling after infarction, based on its anatomy and mapped to motor function (with DSCT as the key pathway mediating/moderating prognosis), serves as a potent biomarker for lower limb prognosis and a basis for precise rehabilitation.

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AAAI Conference 2026 Conference Paper

Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection

  • Yuanzhe Peng
  • Wenwei Zhao
  • Zhuo Lu
  • Jie Xu

Vertical Federated Learning (VFL) enables multiple clients with feature-partitioned data to collaboratively train models while preserving privacy by transmitting embeddings instead of raw data. However, such embeddings can still expose sensitive attributes (e.g., gender or race) unrelated to the target task, making them vulnerable to attribute inference attacks. Most existing privacy strategies may provide extra protection, but at the cost of reduced accuracy and excessive privacy budget. In this paper, we propose a novel equilibrium-driven VFL framework with selective privacy protection for sensitive attributes that are difficult to isolate from embeddings, thereby enhancing local privacy with minor accuracy compromise. We introduce two key innovations: (1) a NashCoder, which incorporates a surrogate head to jointly optimize accuracy and privacy; (2) an adaptive decomposition strategy based on Shapley values, which dynamically decomposes the global objective for distributed optimization from an equilibrium perspective. We theoretically analyze our framework and empirically evaluate it on three public datasets against five baselines, demonstrating significant improvements in the accuracy-privacy trade-off under various privacy settings. Extensive experimental results support our theoretical analysis.

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AAAI Conference 2026 Conference Paper

FedALT: Federated Fine-Tuning Through Adaptive Local Training with Rest-of-World LoRA

  • Jieming Bian
  • Lei Wang
  • Letian Zhang
  • Jie Xu

Fine-tuning large language models (LLMs) in federated settings enables privacy-preserving adaptation but suffers from cross-client interference due to model aggregation. Existing federated LoRA fine-tuning methods, primarily based on FedAvg, struggle with data heterogeneity, leading to harmful cross-client interference and suboptimal personalization. In this work, we propose FedALT, a novel personalized federated LoRA fine-tuning algorithm that fundamentally departs from FedAvg. Instead of using an aggregated model to initialize local training, each client continues training its individual LoRA while incorporating shared knowledge through a separate Rest-of-World (RoW) LoRA component. To effectively balance local adaptation and global information, FedALT introduces an adaptive mixer that dynamically learns input-specific weightings between the individual and RoW LoRA components, drawing conceptual foundations from the Mixture-of-Experts (MoE) paradigm. Through extensive experiments on NLP benchmarks, we demonstrate that FedALT significantly outperforms state-of-the-art personalized federated LoRA fine-tuning methods, achieving superior local adaptation without sacrificing computational efficiency.

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AAAI Conference 2026 Conference Paper

Graph Smoothing for Enhanced Local Geometry Learning in Point Cloud Analysis

  • Shangbo Yuan
  • Jie Xu
  • Ping Hu
  • Xiaofeng Zhu
  • Na Zhao

Graph-based methods have proven to be effective in capturing relationships among points for 3D point cloud analysis. However, these methods often suffer from suboptimal graph structures, particularly due to sparse connections at boundary points and noisy connections in junction areas. To address these challenges, we propose a novel method that integrates a graph smoothing module with an enhanced local geometry learning module. Specifically, we identify the limitations of conventional graph structures, particularly in handling boundary points and junction areas. In response, we introduce a graph smoothing module designed to optimize the graph structure and minimize the negative impact of unreliable sparse and noisy connections. Based on the optimized graph structure, we improve the feature extract function with local geometry information. These include shape features derived from adaptive geometric descriptors based on eigenvectors and distribution features obtained through cylindrical coordinate transformation. Experimental results on real-world datasets validate the effectiveness of our method in various point cloud learning tasks, i.e., classification, part segmentation, and semantic segmentation.

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AAMAS Conference 2026 Conference Paper

Learning Robust Policy for Multi-UAV Collision Avoidance via Compact Causal Feature

  • Zhun Fan
  • Gaofei Han
  • Che Lin
  • Wenji Li
  • Jie Xu
  • Jiafan Zhuang

Deepreinforcementlearning(DRL)-basedmulti-UAVcollisionavoidance methods often exhibit limited generalization when deployed in unseen environments, primarily due to the reliance on noncausal and redundant visual features. Such overfitting to spurious correlations compromises both robustness and safety during realworld deployment. To address these limitations, this study proposes a novel Compact Causal Feature Learning (CCFL) framework that enables UAVs to learn compact and generalizable causal representations. Specifically, a Causal Feature Identification module is designed to disentangle input representations into causal and noncausalcomponents, ensuringthatthelearnedfeaturespreservetrue environmental causality. Furthermore, a Redundancy Feature Compression module is introduced to remove redundant dependencies and compact the causal subspace, thereby enhancing generalization to previously unseen scenarios. Extensive experiments on a challenging UAV collision avoidance benchmark demonstrate that CCFL achieves substantial performance gains over state-of-the-art baselines, increasing individual success rates by 42. 0% and swarm success rates by 61. 6%. These results validate the effectiveness of compact causal feature learning for improving the adaptability, robustness, and safety of autonomous UAV systems operating in complex dynamic environments.

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AAAI Conference 2026 Conference Paper

Mamba-Driven Multi-View Discriminative Clustering via Global-Local Cross-View Sequence Modeling

  • Yuanyang Zhang
  • Xinhang Wan
  • Chao Zhang
  • Jie Xu
  • Cunjian Chen
  • Tien-Tsin Wong
  • Li Yao
  • Yijie Lin

Multi-view clustering (MVC) has recently garnered increasing attention for its ability to partition unlabeled samples into distinct clusters by leveraging complementary and consistent information from different views. Existing MVC methods primarily combine deep neural networks with contrastive learning for cross-view representation learning, yet often overlook the inherent global-local structural relationships among samples. While GNN-based methods capture local structures, they struggle to model global dependencies, leading to inferior inter-cluster separability. In contrast, Transformer-based methods excel at global aggregation but suffer from quadratic complexity, and their attention smoothing effect weakens fine-grained local structures, resulting in suboptimal intra-cluster compactness. To address these limitations, we propose a novel end-to-end MVC framework called Mamba-Driven Multi-View Discriminative Clustering via Global-Local Cross-View Sequence Modeling (MGLC). By flexibly constructing multi-view sequences, MGLC fully exploits the efficient sequence modeling capabilities of Mamba to jointly model cross-view dependencies and global-local structural relationships among samples. Furthermore, MGLC introduces a Cross-Mamba Fusion module to dynamically integrate cross-view and global-local structural representations. Additionally, MGLC incorporates a Dual Calibration Contrastive Learning module, guided by high-confidence pseudo-labels, that adaptively refines both feature and semantic representations while mitigating false negatives among semantically similar samples. Extensive comparative experiments and ablation studies demonstrate the effectiveness of MGLC.

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TMLR Journal 2026 Journal Article

MV2MAE: Self-Supervised Video Pre-Training with Motion-Aware Multi-View Masked Autoencoders

  • Ketul Shah
  • Robert Crandall
  • Jie Xu
  • Peng Zhou
  • Vipin Pillai
  • Marian George
  • Mayank Bansal
  • Rama Chellappa

Videos captured from multiple viewpoints can help in perceiving the 3D structure of the world and benefit computer vision tasks such as action recognition, tracking, etc. In this paper, we present MV2MAE, a method for self-supervised learning from synchronized multi-view videos, built on the masked autoencoder framework. We introduce two key enhancements to better exploit multi-view video data. First, we design a cross-view reconstruction task that leverages a cross-attention-based decoder to reconstruct a target viewpoint video from source view. This helps in effectively injecting geometric information and yielding representations robust to viewpoint changes. Second, we introduce a controllable motion-weighted reconstruction loss which emphasizes dynamic regions and mitigates trivial reconstruction of static backgrounds. This improves temporal modeling and encourages learning more meaningful representations across views. MV2MAE achieves state-of-the-art results on the NTU-60, NTU-120 and ETRI datasets among self-supervised approaches. In the more practical transfer learning setting, it delivers consistent gains of +2.0 -- 8.5% on NUCLA, PKU-MMD-II and ROCOG-v2 datasets, demonstrating the robustness and generalizability of our approach. Code: https://github.com/kshah33/mv2mae

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TMLR Journal 2026 Journal Article

ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning

  • Shangqian Gao
  • Ting Hua
  • Reza Shirkavand
  • Chi-Heng Lin
  • Zheng Tang
  • Zhengao Li
  • Longge Yuan
  • Fangyi Li

Large Language Models (LLMs) demonstrate remarkable capabilities but face deployment challenges due to their high computational demands. Traditional pruning methods reduce these costs by permanently removing parameters, which inevitably leads to performance degradation. To mitigate this issue, we propose ToMoE, a method that transforms dense LLMs into Mixture-of-Experts (MoE) models by uncovering experts inherently present within dense models, without requiring any weight updates. ToMoE leverages dynamic structural pruning to unify expert construction and router training in a single stage, achieving consistently strong performance. Remarkably, even without fine-tuning \revise{the model weights}, ToMoE consistently outperforms state-of-the-art pruning and MoE techniques across Phi-2, LLaMA-2, LLaMA-3, and Qwen-2.5 models. The code for this paper is available at https://github.com/gaosh/ToMoE.

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AAAI Conference 2026 Conference Paper

Views Attention Fusion of Granular-ball Fuzzy Representations Split for Improved Multi-view Clustering

  • Shuaiyu Liu
  • Song Wu
  • Jie Xu
  • Yazhou Ren
  • Yang Yang
  • Xiaorong Pu
  • Guoying Wang

Multi-View Clustering (MVC) is a pivotal multi-view learning paradigm widely adopted across various fields. Despite recent advances, existing methods primarily focus on enhancing the performance of fused multi-view representation, often neglecting the issue of Representation Degradation (RD) arising from discrepancies in the intrinsic quality of different views. To address the limitations, we propose a novel Granular-ball Fuzzy Split and Attention Fusion (GFSAF) learning, which leverages the nature of granular-ball to extract mutual and complementary representation separately. Meanwhile, the proposed method introduces an attention variant for fused representations to mitigate the RD issue. GFSAF mainly consists of two training stages: Split-Extract Stage and Views-Fusion Stage. Specifically, we design a novel Granular-ball Fuzzy Contrastive Learning to extract mutual representation, and introduce Noise Stripping Loss to reduce the influence of noise for complementary representation. Then, a novel multi-head Cross Views Attention is proposed to employ attention mechanism from multi-view perspectives for comprehensive fused representations. Experimental results on eight databases demonstrate that our GFSAF achieves superior performance compared to several state-of-the-art MVC methods.

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NeurIPS Conference 2025 Conference Paper

Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning

  • Lei Wang
  • Jieming Bian
  • Letian Zhang
  • Jie Xu

Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks, but fine-tuning them for domain-specific applications often requires substantial domain-specific data that may be distributed across multiple organizations. Federated Learning (FL) offers a privacy-preserving solution, but faces challenges with computational constraints when applied to LLMs. Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient fine-tuning approach, though a single LoRA module often struggles with heterogeneous data across diverse domains. This paper addresses two critical challenges in federated LoRA fine-tuning: 1. determining the optimal number and allocation of LoRA experts across heterogeneous clients, and 2. enabling clients to selectively utilize these experts based on their specific data characteristics. We propose FedLEASE (Federated adaptive LoRA Expert Allocation and SElection), a novel framework that adaptively clusters clients based on representation similarity to allocate and train domain-specific LoRA experts. It also introduces an adaptive top-$M$ Mixture-of-Experts mechanism that allows each client to select the optimal number of utilized experts. Our extensive experiments on diverse benchmark datasets demonstrate that FedLEASE significantly outperforms existing federated fine-tuning approaches in heterogeneous client settings while maintaining communication efficiency.

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TIST Journal 2025 Journal Article

Advancing Session-Based Recommendations with Atten-Mixer+: Dynamic and Adaptive Multi-Level Intent Mining

  • Peiyan Zhang
  • Jiayan Guo
  • Chaozhuo Li
  • Liying Kang
  • Jaeboum Kim
  • Jie Xu
  • Xi Zhang
  • Yan Zhang

Session-Based Recommendation (SBR) systems, traditionally reliant on complex Graph Neural Networks (GNNs), often face challenges with marginal performance improvements despite increased model complexity. In this article, we dissect the classical GNN-based SBR models and empirically find that the sophisticated GNN propagations might be redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we introduce Atten-Mixer+, an advanced iteration of our previously developed Multi-Level Attention Mixture Network (Atten-Mixer). Atten-Mixer+ forgoes GNN propagation in favor of a dynamic and adaptive readout process, tailored to the unique characteristics of each session. Different from the vanilla version, Atten-Mixer+ features the Adaptive Intent Scaler (AIS) layer, which dynamically determines the depth of multi-level user intent analysis and a soft allocation approach for generating user intent queries across entire user interaction sequences. This innovative design allows Atten-Mixer+ to capture a nuanced and comprehensive understanding of user behaviors, overcoming the limitations of fixed-length analysis. Empirical evaluations on benchmark datasets highlight Atten-Mixer+’s superior efficiency and effectiveness, marking a significant step forward in the predictive accuracy of SBR systems.

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AAAI Conference 2025 Conference Paper

Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities

  • Chengkun Sun
  • Jinqian Pan
  • Zhuoli Jin
  • Russell Stevens Terry
  • Jiang Bian
  • Jie Xu

Training deep Convolutional Neural Networks (CNNs) presents unique challenges, including the pervasive issue of elimination singularities—consistent deactivation of nodes leading to degenerate manifolds within the loss landscape. These singularities impede efficient learning by disrupting feature propagation. To mitigate this, we introduce Pool Skip, an architectural enhancement that strategically combines a Max Pooling, a Max Unpooling, a 3 × 3 convolution, and a skip connection. This configuration helps stabilize the training process and maintain feature integrity across layers. We also propose the Weight Inertia hypothesis, which underpins the development of Pool Skip, providing theoretical insights into mitigating degradation caused by elimination singularities through dimensional and affine compensation. We evaluate our method on a variety of benchmarks, focusing on both 2D natural and 3D medical imaging applications, including tasks such as classification and segmentation. Our findings highlight Pool Skip's effectiveness in facilitating more robust CNN training and improving model performance.

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AAAI Conference 2025 Conference Paper

BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic Models

  • Chengkun Sun
  • Jinqian Pan
  • Russell Stevens Terry
  • Jiang Bian
  • Jie Xu

Generative models can enhance discriminative classifiers by constructing complex feature spaces, thereby improving performance on intricate datasets. Conventional methods typically augment datasets with more detailed feature representations or increase dimensionality to make nonlinear data linearly separable. Utilizing a generative model solely for feature space processing falls short of unlocking its full potential within a classifier and typically lacks a solid theoretical foundation. We base our approach on a novel hypothesis: the probability information (logit) derived from a single model training can be used to generate the equivalent of multiple training sessions. Leveraging the central limit theorem, this synthesized probability information is anticipated to converge toward the true probability more accurately. To achieve this goal, we propose the Bernoulli-Gaussian Decision Block (BGDB), a novel module inspired by the Central Limit Theorem and the concept that the mean of multiple Bernoulli trials approximates the probability of success in a single trial. Specifically, we utilize Improved Denoising Diffusion Probabilistic Models (IDDPM) to model the probability of Bernoulli Trials. Our approach shifts the focus from reconstructing features to reconstructing logits, transforming the logit from a single iteration into logits analogous to those from multiple experiments. We provide the theoretical foundations of our approach through mathematical analysis and validate its effectiveness through experimental evaluation using various datasets for multiple imaging tasks, including both classification and segmentation.

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NeurIPS Conference 2025 Conference Paper

Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients

  • Shiyuan Zuo
  • Xingrun Yan
  • Rongfei Fan
  • Li Shen
  • Puning Zhao
  • Jie Xu
  • Han Hu

Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but is vulnerable to Byzantine attacks and data heterogeneity, which can severely degrade performance. Existing Byzantine-robust approaches tackle data heterogeneity, but incur high computational overhead during gradient aggregation, thereby slowing down the training process. To address this issue, we propose a simple yet effective Federated Normalized Gradients Algorithm (Fed-NGA), which performs aggregation by merely computing the weighted mean of the normalized gradients from each client. This approach yields a favorable time complexity of $\mathcal{O}(pM)$, where $p$ is the model dimension and $M$ is the number of clients. We rigorously prove that Fed-NGA is robust to both Byzantine faults and data heterogeneity. For non-convex loss functions, Fed-NGA achieves convergence to a neighborhood of stationary points under general assumptions, and further attains zero optimality gap under some mild conditions, which is an outcome rarely achieved in existing literature. In both cases, the convergence rate is $\mathcal{O}(1/T^{\frac{1}{2} - \delta})$, where $T$ denotes the number of iterations and $\delta \in (0, 1/2)$. Experimental results on benchmark datasets confirm the superior time efficiency and convergence performance of Fed-NGA over existing methods.

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EAAI Journal 2025 Journal Article

Enhanced knowledge graph cascade learning model for cyber–physical systems

  • Shumao Zhang
  • Jie Xu
  • Haodiao Xie
  • Qiuru Fu
  • Ke Miao
  • Shixue Cheng
  • Zelei Wu

Recently, the application prospects of knowledge graph technology in cyber–physical systems (CPS) have attracted considerable attention. However, knowledge graph data in various CPS domains are typically collected from sensors or through manual efforts, which inevitably results in incomplete and unreliable data, thereby impacting the performance of downstream task models. This issue is often overlooked in existing studies. This paper proposes an enhanced knowledge graph cascade learning model for CPS. The model performs cascaded and iterative learning of both graph structure and graph representation. By optimizing the graph structure and incorporating hierarchical learning of graph-structured information, the proposed model enhances feature propagation and aggregation during representation learning. Experiments show that our model achieves outstanding results: compared to the baseline models, our approach achieves an average improvement of 2. 7% in accuracy on the node classification task and 1. 35% in MRR on the link prediction task.

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NeurIPS Conference 2025 Conference Paper

FedEL: Federated Elastic Learning for Heterogeneous Devices

  • Letian Zhang
  • Bo Chen
  • Jieming Bian
  • Lei Wang
  • Jie Xu

Federated learning (FL) enables distributed devices to collaboratively train machine learning (ML) models while maintaining data privacy. However, the heterogeneous hardware capabilities of participating devices often result in significant training delays, as straggler clients with limited resources prolong the aggregation process. Existing solutions such as client selection, asynchronous FL, and partial training partially address these challenges but encounter issues such as reduced accuracy, stale updates, and compromised model performance due to inconsistent training contributions. To overcome these limitations, we propose FedEL, a federated elastic learning framework that enhances training efficiency while maintaining model accuracy. FedEL introduces a novel window-based training process, sliding the window to locate the training part of the model and dynamically selecting important tensors for training within a coordinated runtime budget. This approach ensures progressive and balanced training across all clients, including stragglers. Additionally, FedEL employs a tensor importance adjustment module, harmonizing local and global tensor importance to mitigate biases caused by data heterogeneity. The experiment results shows that FedEL achieves up to 3. 87× improvement in time-to-accuracy compared to baselines while maintaining or exceeding final test accuracy.

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IJCAI Conference 2025 Conference Paper

Graph Embedded Contrastive Learning for Multi-View Clustering

  • Hongqing He
  • Jie Xu
  • Guoqiu Wen
  • Yazhou Ren
  • Na Zhao
  • Xiaofeng Zhu

Recently, numerous multi-view clustering (MVC) and multi-view graph clustering (MVGC) methods have been proposed. Despite significant progress, they still face two issues: I) MVC and MVGC are often developed independently for multi-view and multi-graph data. They have redundancy but lack a unified methodology to combine their strengths. II) Contrastive learning is usually adopted to explore the associations across multiple views. However, traditional contrastive losses ignore the neighbor relationship in multi-view scenarios and easily lead to false associations in sample pairs. To address these issues, we propose Graph Embedded Contrastive Learning for Multi-View Clustering. Concretely, we propose a process of view-specific pre-training with adaptive graph convolution to make our method compatible with both multi-view and multi-graph data, which aggregates the graph information into data and leverages autoencoders to learn view-specific representations. Furthermore, to explore the view-cross associations, we introduce the process of view-cross contrastive learning and clustering, where we propose the graph-guided contrastive learning that can generate global graph to mitigate the false association issue as well as the cluster-guided contrastive clustering for improving the model robustness. Finally, extensive experiments demonstrate that our method achieves superior performance on both MVC and MVGC tasks.

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AAAI Conference 2025 Conference Paper

Incomplete Multi-view Clustering via Diffusion Contrastive Generation

  • Yuanyang Zhang
  • Yijie Lin
  • Weiqing Yan
  • Li Yao
  • Xinhang Wan
  • Guangyuan Li
  • Chao Zhang
  • Guanzhou Ke

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved significant improvements, they still encounter notable limitations: 1) heavy reliance on paired data for training the data recovery module, which is impractical in real scenarios with high missing data rates; 2) the generated data often lacks diversity and discriminability, resulting in suboptimal clustering results. To address these shortcomings, we propose a novel IMVC method called Diffusion Contrastive Generation (DCG). Motivated by the consistency between the diffusion and clustering processes, DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data. By performing contrastive learning on a limited set of paired multi-view samples, DCG can align the generated views with the real views, facilitating accurate recovery of views across arbitrary missing view scenarios. Additionally, DCG integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data, achieving robust and end-to-end clustering. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches.

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JBHI Journal 2025 Journal Article

Interpretable End to End Epileptic Seizure Detection via Linear and Nonlinear Filtering Networks

  • Jie Wang
  • Xianlei Zeng
  • Yingchao Wang
  • Jie Xu
  • Defu Zhai
  • Han Xiao
  • Weiwei Nie
  • Qi Yuan

Epilepsy is a prevalent neurological disorder marked recurrent, unpredictable seizures. Electroencephalogram (EEG)-based seizure detection has become a key focus in clinical research due to its potential for identifying abnormal brain activity patterns. However, most current approaches rely on single-modal feature analysis and struggle to disentangle the complex linear and nonlinear dynamics of EEG signals, limiting their clinical utility. To address this limitation, we propose a novel contrastive learning framework with linear and nonlinear filtering networks (CL LNFNet) for interpretable seizure detection. CL-LNFNet enhances explainability by tracing the full decision-making pathway from raw EEG signals to diagnostic outcomes. Through comparative analysis of feature evolution across six seizure types and non-seizure states, the model bridges the gap between the “black-box” nature of deep learning and the transparency required in clinical diagnostics. The framework first employs a recursive residual decomposition scheme to extract linear and nonlinear components using dual-branch decoupling networks. These features are then refined via two adaptive filtering networks equipped with feature selection gating mechanisms. A multi-scale convolutional module within a three-layer convolutional architecture hierarchically integrates the dual-stream outputs to improve classification performance. Furthermore, we introduce a hybrid learning strategy that combines supervised and self-supervised contrastive learning to enhance feature representation through the joint optimization of both loss functions. Experimental evaluations on both scalp and intracranial EEG datasets demonstrate that CL-LNFNet achieves over 95% accuracy in both cross-patient and specific patient scenarios, outperforming existing state-of-the-art methods. The code is available at https://github.com/JW Image/CL-LNFNet.

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ICRA Conference 2025 Conference Paper

Large-Scale UWB Anchor Calibration and One-Shot Localization Using Gaussian Process

  • Shenghai Yuan 0001
  • Boyang Lou
  • Thien-Minh Nguyen
  • Pengyu Yin
  • Muqing Cao
  • Xinghang Xu
  • Jianping Li 0004
  • Jie Xu

Ultra-wideband (UWB) is gaining popularity with devices like AirTags for precise home item localization but faces significant challenges when scaled to large environments like seaports. The main challenges are calibration and localization under obstructed conditions, which are common in logistics environments. Traditional calibration methods, dependent on line-of-sight (LoS), are slow, costly, and unreliable in seaports and warehouses, making large-scale localization a significant pain point in the industry. To overcome these challenges, we propose a one-shot calibration and localization framework based on UWB-LiDAR fusion. Our method uses Gaussian processes to estimate the anchor position from continuous-time LiDAR Inertial Odometry with sampled UWB ranges. This approach ensures accurate and reliable calibration with only one round of sampling in large-scale areas, i. e. , $600 \times 450 ~\mathrm{m}^{2}$. With LoS issues, UWB-only localization can be problematic, even when anchor positions are known. We demonstrate that by applying a UWB-range filter, the search range for LiDAR loop closure descriptors is significantly reduced, improving both accuracy and speed. This concept can be applied to other loop closure detection methods, enabling cost-effective localization in large-scale warehouses and seaports. It significantly improves precision in challenging environments where the UWB-only and LiDAR-Inertial methods fail, as shown in the video https://https://youtu.be/oY8jQKdM7lU.We will open-source our datasets and calibration codes for community use.

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IROS Conference 2025 Conference Paper

PLK-Calib: Single-shot and Target-less LiDAR-Camera Extrinsic Calibration using Plücker Lines

  • Yanyu Zhang
  • Jie Xu
  • Wei Ren

Accurate LiDAR-Camera (LC) calibration is challenging but crucial for autonomous systems and robotics. In this paper, we propose two single-shot and target-less algorithms to estimate the calibration parameters between LiDAR and camera using line features. The first algorithm constructs line-to-line constraints by defining points-to-line projection errors and minimizes the projection error. The second algorithm (PLK-Calib) utilizes the co-perpendicular and co-parallel geometric properties of lines in Plücker (PLK) coordinate, and decouples the rotation and translation into two constraints, enabling more accurate estimates. Our degenerate analysis and Monte Carlo simulation indicate that three nonparallel line pairs are the minimal requirements to estimate the extrinsic parameters. Furthermore, we collect an LC calibration dataset with varying extrinsic under three different scenarios and use it to evaluate the performance of our proposed algorithms.

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ICLR Conference 2025 Conference Paper

Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation

  • Chenbin Zhang
  • Zhiqiang Hu
  • Chuchu Jiang
  • Wen Chen 0022
  • Jie Xu
  • Shaoting Zhang 0001

Drug-target binding affinity prediction is a fundamental task for drug discovery. It has been extensively explored in literature and promising results are reported. However, in this paper, we demonstrate that the results may be misleading and cannot be well generalized to real practice. The core observation is that the canonical randomized split of a test set in conventional evaluation leaves the test set dominated by samples with high similarity to the training set. The performance of models is severely degraded on samples with lower similarity to the training set but the drawback is highly overlooked in current evaluation. As a result, the performance can hardly be trusted when the model meets low-similarity samples in real practice. To address this problem, we propose a framework of similarity aware evaluation in which a novel split methodology is proposed to adapt to any desired distribution. This is achieved by a formulation of optimization problems which are approximately and efficiently solved by gradient descent. We perform extensive experiments across five representative methods in four datasets for two typical target evaluations and compare them with various counterpart methods. Results demonstrate that the proposed split methodology can significantly better fit desired distributions and guide the development of models.

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IROS Conference 2025 Conference Paper

Robust Online Calibration for UWB-Aided Visual-Inertial Navigation with Bias Correction

  • Yizhi Zhou
  • Jie Xu
  • Jiawei Xia
  • Zechen Hu
  • Weizi Li
  • Xuan Wang 0013

This paper presents a novel robust online calibration framework for Ultra-Wideband (UWB) anchors in UWB-aided Visual-Inertial Navigation Systems (VINS). Accurate anchor positioning, a process known as calibration, is crucial for integrating UWB ranging measurements into state estimation. While several prior works have demonstrated satisfactory results by using robot-aided systems to autonomously calibrate UWB systems, there are still some limitations: 1) these approaches assume accurate robot localization during the initialization step, ignoring localization errors that can compromise calibration robustness, and 2) the calibration results are highly sensitive to the initial guess of the UWB anchors’ positions, reducing the practical applicability of these methods in real-world scenarios. Our approach addresses these challenges by explicitly incorporating the impact of robot localization uncertainties into the calibration process, ensuring robust initialization. To further enhance the robustness of the calibration results against initialization errors, we propose a tightly-coupled Schmidt Kalman Filter (SKF)-based online refinement method, making the system suitable for practical applications. Simulations and real-world experiments validate the improved accuracy and robustness of our approach.

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NeurIPS Conference 2024 Conference Paper

Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views

  • Xinyue Chen
  • Yazhou Ren
  • Jie Xu
  • Fangfei Lin
  • Xiaorong Pu
  • Yang Yang

Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Many existing approaches tend to assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients. While these methods have succeeded, they may encounter challenges in practical FedMVC scenarios involving heterogeneous hybrid views, where a mixture of single-view and multi-view clients exhibit varying degrees of heterogeneity. In this paper, we propose a novel FedMVC framework, which concurrently addresses two challenges associated with heterogeneous hybrid views, i. e. , client gap and view gap. To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients. To address the view gap, we develop a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views. The interplay between local-synergistic contrastive learning and global-specific weighting aggregation mutually enhances the exploration of the data cluster structures distributed on multiple clients. Theoretical analysis and extensive experiments demonstrate that our method can handle the heterogeneous hybrid views in FedMVC and outperforms state-of-the-art methods.

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ICRA Conference 2024 Conference Paper

Experience Consistency Distillation Continual Reinforcement Learning for Robotic Manipulation Tasks

  • Chao Zhao
  • Jie Xu
  • Ru Peng
  • Xingyu Chen
  • Kuizhi Mei
  • Xuguang Lan

Continual reinforcement learning, which aims to help robots acquire skills without catastrophic forgetting, obviating the need to re-learn all tasks from scratch. In order to enable lifelong acquisition of skills in robots, replay-based continual reinforcement learning has emerged as a promising research direction. These techniques replay data from previous tasks to mitigate forgetting when learning new skills. However, existing replay-based methods store poor representative experience, and the experience utilization of old tasks is inefficient. To address these issues, we propose an experience consistency distillation method for robot continual reinforcement learning to improve the data efficiency of the experience. Specifically, the experience of old tasks are distilled to obtain Markov Decision Process (MDP) data with high compression ratio and information content. To ensure consistent data distributions before and after distillation, we further utilize a Fréchet Inception Distance (FID) loss as a regularization constraint. In order to improve experience utilization efficiency, the policy is then trained using both the distilled data and current task data, with policy distillation performed based on uncertainty metrics. Our method is validated in the continual reinforcement learning simulation platform and real scene with a UR5e robot arm. Experimental results indicate that our method achieves higher success and lower buffer size requirement compared to other methods.

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IROS Conference 2024 Conference Paper

I 2 EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry

  • Wenlu Yu
  • Jie Xu
  • Chengwei Zhao
  • Lijun Zhao 0003
  • Thien-Minh Nguyen
  • Shenghai Yuan 0001
  • Mingming Bai
  • Lihua Xie 0001

LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the problem: IEKF only iterates over the observation equation, relying on a rough estimate of the initial state, which is insufficient to fully eliminate motion distortion in the input point cloud; the system process noise is difficult to be determined during state estimation of the complex motions; and the varying motion models across different sensor carriers. To address these issues, we propose the Dual-Iteration Extended Kalman Filter (I 2 EKF) and the LiDAR odometry based on I 2 EKF (I 2 EKF-LO). This approach not only iterates over the observation equation but also leverages state updates to iteratively mitigate motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts process noise based on the confidence level of prior predictions during state estimation and establishes motion models for different sensor carriers to achieve accurate and efficient state estimation. Comprehensive experiments demonstrate that I 2 EKF-LO achieves outstanding levels of accuracy and computational efficiency in the realm of LiDAR odometry. Additionally, to foster community development, our code is open-sourced. 1

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NeurIPS Conference 2024 Conference Paper

MeMo: Meaningful, Modular Controllers via Noise Injection

  • Megan Tjandrasuwita
  • Jie Xu
  • Armando Solar-Lezama
  • Wojciech Matusik

Robots are often built from standardized assemblies, (e. g. arms, legs, or fingers), but each robot must be trained from scratch to control all the actuators of all the parts together. In this paper we demonstrate a new approach that takes a single robot and its controller as input and produces a set of modular controllers for each of these assemblies such that when a new robot is built from the same parts, its control can be quickly learned by reusing the modular controllers. We achieve this with a framework called MeMo which learns (Me)aningful, (Mo)dular controllers. Specifically, we propose a novel modularity objective to learn an appropriate division of labor among the modules. We demonstrate that this objective can be optimized simultaneously with standard behavior cloning loss via noise injection. We benchmark our framework in locomotion and grasping environments on simple to complex robot morphology transfer. We also show that the modules help in task transfer. On both structure and task transfer, MeMo achieves improved training efficiency to graph neural network and Transformer baselines.

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IJCAI Conference 2024 Conference Paper

Simple Contrastive Multi-View Clustering with Data-Level Fusion

  • Caixuan Luo
  • Jie Xu
  • Yazhou Ren
  • Junbo Ma
  • Xiaofeng Zhu

Previous deep multi-view clustering methods usually design un-shared encoders to explore the cluster information among multi-view data, but they are difficult to customize the encoders for individual views and easily increase information loss. To address these issues, we propose a simple yet effective contrastive multi-view clustering framework. Specifically, different from using feature-level fusion in previous methods, we first propose a data-level fusion method to fuse multi-view information, which produces a fused data to replace all views and thus avoids customizing networks for different views. Then, we simulate the data noise and unavailability in multiple views to design two kinds of data augmentation for the fused data, making a shared encoder with simple contrastive learning to learn robust features and achieve the interaction across views. As a result, our method is a general framework and we base on it to conduct feature clustering and end-to-end clustering. Extensive experiments demonstrate that our method can explore the discriminative information in multi-view data and achieve superior clustering performance.

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JBHI Journal 2024 Journal Article

Spatiotemporal Network Based on GCN and BiGRU for Seizure Detection

  • Jie Xu
  • Shasha Yuan
  • Junliang Shang
  • Juan Wang
  • Kuiting Yan
  • Yankai Yang

As an important tool for detecting and diagnosing epilepsy, multi-channel EEG records the neuronal activities of different brain regions. Visual identification of abnormal EEG signals poses challenges, making the use of artificial intelligence techniques for automated seizure detection an inevitable trend. However, existing seizure detection methods often overlook the spatial relationship between EEG channels, which can't take full advantage of brain network structure. In this paper, we design an end-to-end spatiotemporal architecture for seizure detection based on Graph Convolutional Networks (GCN) and Bidirectional Gated Recurrent Units (BiGRU) to efficiently model the spatial dependence and temporal dynamics of EEG. Firstly, the original EEG signals are preprocessed by applying wavelet transform for temporal-frequency analysis. The Pearson correlation matrix is computed for specific frequency bands and GCN is utilized to extract spatial features between EEG channels. Then, these features are sent into the BiGRU network to capture temporal relationships. Finally, the detection decisions are achieved using fully connected layers and the multi-level decision rules are implemented to provide the final results. The proposed method is validated on CHB-MIT EEG dataset, achieving 98. 85% sensitivity, 95. 83% specificity, 97. 35% accuracy, 97. 4% F1-score, and 97. 33% AUC. This network fusions multiple EEG characteristics in the spatial-temporal-frequency domains to improve the detection performance and the promising result demonstrates that the performance of this model is superior to or on par with existing methods.

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NeurIPS Conference 2024 Conference Paper

Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains

  • Lei Wang
  • Jieming Bian
  • Letian Zhang
  • Chen Chen
  • Jie Xu

Federated learning (FL) allows collaborative machine learning training without sharing private data. While most FL methods assume identical data domains across clients, real-world scenarios often involve heterogeneous data domains. Federated Prototype Learning (FedPL) addresses this issue, using mean feature vectors as prototypes to enhance model generalization. However, existing FedPL methods create the same number of prototypes for each client, leading to cross-domain performance gaps and disparities for clients with varied data distributions. To mitigate cross-domain feature representation variance, we introduce FedPLVM, which establishes variance-aware dual-level prototypes clustering and employs a novel $\alpha$-sparsity prototype loss. The dual-level prototypes clustering strategy creates local clustered prototypes based on private data features, then performs global prototypes clustering to reduce communication complexity and preserve local data privacy. The $\alpha$-sparsity prototype loss aligns samples from underrepresented domains, enhancing intra-class similarity and reducing inter-class similarity. Evaluations on Digit-5, Office-10, and DomainNet datasets demonstrate our method's superiority over existing approaches.

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ICRA Conference 2024 Conference Paper

Towards Unified Interactive Visual Grounding in The Wild

  • Jie Xu
  • Hanbo Zhang
  • Qingyi Si
  • Yifeng Li
  • Xuguang Lan
  • Tao Kong

Interactive visual grounding in Human-Robot Interaction (HRI) is challenging yet practical due to the inevitable ambiguity in natural languages. It requires robots to disambiguate the user’s input by active information gathering. Previous approaches often rely on predefined templates to ask disambiguation questions, resulting in performance reduction in realistic interactive scenarios. In this paper, we propose TiO, an end-to-end system for interactive visual grounding in human-robot interaction. Benefiting from a unified formulation of visual dialog and grounding, our method can be trained on a joint of extensive public data, and show superior generality to diversified and challenging open-world scenarios. In the experiments, we validate TiO on GuessWhat? ! and InViG benchmarks, setting new state-of-the-art performance by a clear margin. Moreover, we conduct HRI experiments on the carefully selected 150 challenging scenes as well as real-robot platforms. Results show that our method demonstrates superior generality to diversified visual and language inputs with a high success rate. Codes and demos are available on https://jxu124.github.io/TiO/.

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ICLR Conference 2024 Conference Paper

Vision-Language Foundation Models as Effective Robot Imitators

  • Xinghang Li
  • Minghuan Liu
  • Hanbo Zhang
  • Cunjun Yu
  • Jie Xu
  • Hongtao Wu
  • Chilam Cheang
  • Ya Jing

Recent progress in vision language foundation models has shown their ability to understand multimodal data and resolve complicated vision language tasks, including robotics manipulation. We seek a straightforward way of making use of existing vision-language models (VLMs) with simple fine-tuning on robotics data. To this end, we derive a simple and novel vision-language manipulation framework, dubbed RoboFlamingo, built upon the open-source VLMs, OpenFlamingo. Unlike prior works, RoboFlamingo utilizes pre-trained VLMs for single-step vision-language comprehension, models sequential history information with an explicit policy head, and is slightly fine-tuned by imitation learning only on language-conditioned manipulation datasets. Such a decomposition provides RoboFlamingo the flexibility for open-loop control and deployment on low-performance platforms. By exceeding the state-of-the-art performance with a large margin on the tested benchmark, we show RoboFlamingo can be an effective and competitive alternative to adapt VLMs to robot control. Our extensive experimental results also reveal several interesting conclusions regarding the behavior of different pre-trained VLMs on manipulation tasks. We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy. Our code will be made public upon acceptance.

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JBHI Journal 2023 Journal Article

Cross-Hospital Sepsis Early Detection via Semi-Supervised Optimal Transport With Self-Paced Ensemble

  • Ruiqing Ding
  • Yu Zhou
  • Jie Xu
  • Yan Xie
  • Qiqiang Liang
  • He Ren
  • Yixuan Wang
  • Yanlin Chen

Leveraging machine learning techniques for Sepsis early detection and diagnosis has attracted increasing interest in recent years. However, most existing methods require a large amount of labeled training data, which may not be available for a target hospital that deploys a new Sepsis detection system. More seriously, as treated patients are diversified between hospitals, directly applying a model trained on other hospitals may not achieve good performance for the target hospital. To address this issue, we propose a novel semi-supervised transfer learning framework based on optimal transport theory and self-paced ensemble for Sepsis early detection, called SPSSOT, which can efficiently transfer knowledge from the source hospital (with rich labeled data) to the target hospital (with scarce labeled data). Specifically, SPSSOT incorporates a new optimal transport-based semi-supervised domain adaptation component that can effectively exploit all the unlabeled data in the target hospital. Moreover, self-paced ensemble is adapted in SPSSOT to alleviate the class imbalance issue during transfer learning. In a nutshell, SPSSOT is an end-to-end transfer learning method that automatically selects suitable samples from two domains (hospitals) respectively and aligns their feature spaces. Extensive experiments on two open clinical datasets, MIMIC-III and Challenge, demonstrate that SPSSOT outperforms state-of-the-art transfer learning methods by improving 1–3% of AUC.

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AAAI Conference 2023 Conference Paper

Dual Label-Guided Graph Refinement for Multi-View Graph Clustering

  • Yawen Ling
  • Jianpeng Chen
  • Yazhou Ren
  • Xiaorong Pu
  • Jie Xu
  • Xiaofeng Zhu
  • Lifang He

With the increase of multi-view graph data, multi-view graph clustering (MVGC) that can discover the hidden clusters without label supervision has attracted growing attention from researchers. Existing MVGC methods are often sensitive to the given graphs, especially influenced by the low quality graphs, i.e., they tend to be limited by the homophily assumption. However, the widespread real-world data hardly satisfy the homophily assumption. This gap limits the performance of existing MVGC methods on low homophilous graphs. To mitigate this limitation, our motivation is to extract high-level view-common information which is used to refine each view's graph, and reduce the influence of non-homophilous edges. To this end, we propose dual label-guided graph refinement for multi-view graph clustering (DuaLGR), to alleviate the vulnerability in facing low homophilous graphs. Specifically, DuaLGR consists of two modules named dual label-guided graph refinement module and graph encoder module. The first module is designed to extract the soft label from node features and graphs, and then learn a refinement matrix. In cooperation with the pseudo label from the second module, these graphs are refined and aggregated adaptively with different orders. Subsequently, a consensus graph can be generated in the guidance of the pseudo label. Finally, the graph encoder module encodes the consensus graph along with node features to produce the high-level pseudo label for iteratively clustering. The experimental results show the superior performance on coping with low homophilous graph data. The source code for DuaLGR is available at https://github.com/YwL-zhufeng/DuaLGR.

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IROS Conference 2023 Conference Paper

IF-Based Trajectory Planning and Cooperative Control for Transportation System of Cable Suspended Payload With Multi UAVs

  • Yu Zhang 0091
  • Jie Xu
  • Cheng Zhao 0002
  • Jiuxiang Dong

In this paper, we tackle the control and trajectory planning problems for the cooperative transportation system of cable-suspended payload with multi Unmanned Aerial Vehicles (UAVs). Firstly, a payload controller is presented considering the dynamic coupling between the UAV and the payload to accomplish the active suppression of payload swing and the complex payload trajectory tracking. Secondly, different from the simplification of obstacles in most approaches, we propose three Insetting Formation (IF) algorithms for the complete obstacle shape to generate collision-free waypoints for the cooperative transportation system. An IF strategy is proposed by integrating three IF algorithms to improve the success rate of obstacle avoidance and reduce the algorithm complexity for performing the aggressive flight. Finally, we verify the robustness and high performance of the proposed algorithm through benchmark comparison and real-world experiments. Moreover, our source code is released as an open-source ros package.

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NeurIPS Conference 2023 Conference Paper

Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration

  • Jie Xu
  • Shuo Chen
  • Yazhou Ren
  • Xiaoshuang Shi
  • Hengtao Shen
  • Gang Niu
  • Xiaofeng Zhu

Recently, numerous studies have demonstrated the effectiveness of contrastive learning (CL), which learns feature representations by pulling in positive samples while pushing away negative samples. Many successes of CL lie in that there exists semantic consistency between data augmentations of the same instance. In multi-view scenarios, however, CL might cause representation degeneration when the collected multiple views inherently have inconsistent semantic information or their representations subsequently do not capture sufficient discriminative information. To address this issue, we propose a novel framework called SEM: SElf-weighted Multi-view contrastive learning with reconstruction regularization. Specifically, SEM is a general framework where we propose to first measure the discrepancy between pairwise representations and then minimize the corresponding self-weighted contrastive loss, and thus making SEM adaptively strengthen the useful pairwise views and also weaken the unreliable pairwise views. Meanwhile, we impose a self-supervised reconstruction term to regularize the hidden features of encoders, to assist CL in accessing sufficient discriminative information of data. Experiments on public multi-view datasets verified that SEM can mitigate representation degeneration in existing CL methods and help them achieve significant performance improvements. Ablation studies also demonstrated the effectiveness of SEM with different options of weighting strategies and reconstruction terms.

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ICRA Conference 2022 Conference Paper

A Continuous Learning Approach for Probabilistic Human Motion Prediction

  • Jie Xu
  • Shihong Wang
  • Xingyu Chen
  • Jiahao Zhang
  • Xuguang Lan
  • Nanning Zheng 0001

Human Motion Prediction (HMP) plays a crucial role in safe Human-Robot-Interaction (HRI). Currently, the majority of HMP algorithms are trained by massive pre-collected data. As the training data only contains a few pre-defined motion patterns, these methods cannot handle the unfamiliar motion patterns. Moreover, the pre-collected data are usually non-interactive, which does not consider the real-time responses of collaborators. As a result, these methods usually perform unsatisfactorily in real HRI scenarios. To solve this problem, in this paper, we propose a novel Continual Learning (CL) approach for probabilistic HMP which makes the robot continually learns during its interaction with collaborators. The proposed approach consists of two steps. First, we leverage a Bayesian Neural Network to model diverse uncertainties of observed human motions for collecting online interactive data safely. Then we take Experience Replay and Knowledge Distillation to elevate the model with new experiences while maintaining the knowledge learned before. We first evaluate our approach on a large-scale benchmark dataset Human3. 6m. The experimental results show that our approach achieves a lower prediction error compared with the baselines methods. Moreover, our approach could continually learn new motion patterns without forgetting the learned knowledge. We further conduct real-scene experiments using Kinect DK. The results show that our approach can learn the human kinematic model from scratch, which effectively secures the interaction.

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AAAI Conference 2022 Conference Paper

ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification

  • Junfan Chen
  • Richong Zhang
  • Yongyi Mao
  • Jie Xu

Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. Despite their success, existing works building their meta-learner based on Prototypical Networks are unsatisfactory in learning discriminative text representations between similar classes, which may lead to contradictions during label prediction. In addition, the tasklevel and instance-level overfitting problems in few-shot text classification caused by a few training examples are not sufficiently tackled. In this work, we propose a contrastive learning framework named ContrastNet to tackle both discriminative representation and overfitting problems in few-shot text classification. ContrastNet learns to pull closer text representations belonging to the same class and push away text representations belonging to different classes, while simultaneously introducing unsupervised contrastive regularization at both task-level and instance-level to prevent overfitting. Experiments on 8 few-shot text classification datasets show that ContrastNet outperforms the current state-of-the-art models.

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AAAI Conference 2022 Conference Paper

Deep Incomplete Multi-View Clustering via Mining Cluster Complementarity

  • Jie Xu
  • Chao Li
  • Yazhou Ren
  • Liang Peng
  • Yujie Mo
  • Xiaoshuang Shi
  • Xiaofeng Zhu

Incomplete multi-view clustering (IMVC) is an important unsupervised approach to group the multi-view data containing missing data in some views. Previous IMVC methods suffer from the following issues: (1) the inaccurate imputation or padding for missing data negatively affects the clustering performance, (2) the quality of features after fusion might be interfered by the low-quality views, especially the inaccurate imputed views. To avoid these issues, this work presents an imputation-free and fusion-free deep IMVC framework. First, the proposed method builds a deep embedding feature learning and clustering model for each view individually. Our method then nonlinearly maps the embedding features of complete data into a high-dimensional space to discover linear separability. Concretely, this paper provides an implementation of the high-dimensional mapping as well as shows the mechanism to mine the multi-view cluster complementarity. This complementary information is then transformed to the supervised information with high confidence, aiming to achieve the multi-view clustering consistency for the complete data and incomplete data. Furthermore, we design an EM-like optimization strategy to alternately promote feature learning and clustering. Extensive experiments on real-world multi-view datasets demonstrate that our method achieves superior clustering performance over state-of-the-art methods.

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AAAI Conference 2022 Conference Paper

Simple Unsupervised Graph Representation Learning

  • Yujie Mo
  • Liang Peng
  • Jie Xu
  • Xiaoshuang Shi
  • Xiaofeng Zhu

In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the interclass variation, as well as adds an upper bound loss to achieve the finite distance between positive embeddings and anchor embeddings for reducing the intra-class variation. As a result, both enlarging inter-class variation and reducing intra-class variation result in a small generalization error, thereby obtaining an effective model. Furthermore, our method removes widely used data augmentation and discriminator from previous graph contrastive learning methods, meanwhile available to output low-dimensional embeddings, leading to an efficient model. Experimental results on various real-world datasets demonstrate the effectiveness and efficiency of our method, compared to state-of-the-art methods. The source codes are released at https: //github. com/YujieMo/SUGRL.

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NeurIPS Conference 2021 Conference Paper

Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

  • Jagdeep Bhatia
  • Holly Jackson
  • Yunsheng Tian
  • Jie Xu
  • Wojciech Matusik

Both the design and control of a robot play equally important roles in its task performance. However, while optimal control is well studied in the machine learning and robotics community, less attention is placed on finding the optimal robot design. This is mainly because co-optimizing design and control in robotics is characterized as a challenging problem, and more importantly, a comprehensive evaluation benchmark for co-optimization does not exist. In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. In our benchmark, each robot is composed of different types of voxels (e. g. , soft, rigid, actuators), resulting in a modular and expressive robot design space. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. Furthermore, we develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques. Evaluating the algorithms on our benchmark platform, we observe robots exhibiting increasingly complex behaviors as evolution progresses, with the best evolved designs solving many of our proposed tasks. Additionally, even though robot designs are evolved autonomously from scratch without prior knowledge, they often grow to resemble existing natural creatures while outperforming hand-designed robots. Nevertheless, all tested algorithms fail to find robots that succeed in our hardest environments. This suggests that more advanced algorithms are required to explore the high-dimensional design space and evolve increasingly intelligent robots -- an area of research in which we hope Evolution Gym will accelerate progress. Our website with code, environments, documentation, and tutorials is available at http: //evogym. csail. mit. edu/.

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TIST Journal 2021 Journal Article

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs

  • Yuandong Wang
  • Hongzhi Yin
  • Tong Chen
  • Chunyang Liu
  • Ben Wang
  • Tianyu Wo
  • Jie Xu

In recent years, ride-hailing services have been increasingly prevalent, as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in different regions is vital for effective traffic flow control and route planning. As both spatial and temporal patterns are indispensable passenger demand prediction, relevant research has evolved from pure time series to graph-structured data for modeling historical passenger demand data, where a snapshot graph is constructed for each time slot by connecting region nodes via different relational edges (origin-destination relationship, geographical distance, etc.). Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i.e., weights) of passenger demands between two connected regions. aspects in the graph-structure data. representation for DDW is the key to solve the prediction problem. However, existing graph-based solutions fail to simultaneously consider those three crucial aspects of dynamic, directed, and weighted graphs, leading to limited expressiveness when learning graph representations for passenger demand prediction. Therefore, we propose a novel spatiotemporal graph attention network, namely Gallat ( G raph prediction with all at tention) as a solution. In Gallat, by comprehensively incorporating those three intrinsic properties of dynamic directed and weighted graphs, we build three attention layers to fully capture the spatiotemporal dependencies among different regions across all historical time slots. Moreover, the model employs a subtask to conduct pretraining so that it can obtain accurate results more quickly. We evaluate the proposed model on real-world datasets, and our experimental results demonstrate that Gallat outperforms the state-of-the-art approaches.

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ICRA Conference 2021 Conference Paper

Probabilistic Human Motion Prediction via A Bayesian Neural Network

  • Jie Xu
  • Xingyu Chen
  • Xuguang Lan
  • Nanning Zheng 0001

Human motion prediction is an important and challenging topic that has promising prospects in efficient and safe human-robot-interaction systems. Currently, the majority of the human motion prediction algorithms are based on deterministic models, which may lead to risky decisions for robots. To solve this problem, we propose a probabilistic model for human motion prediction in this paper. The key idea of our approach is to extend the conventional deterministic motion prediction neural network to a Bayesian one. On one hand, our model could generate several future motions when given an observed motion sequence. On the other hand, by calculating the Epistemic Uncertainty and the Heteroscedastic Aleatoric Uncertainty, our model could tell the robot if the observation has been seen before and also give the optimal result among all possible predictions. We extensively validate our approach on a large scale benchmark dataset Human3. 6m. The experiments show that our approach performs better than deterministic methods. We further evaluate our approach in a Human-Robot-Interaction (HRI) scenario. The experimental results show that our approach makes the interaction more efficient and safer.

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AAAI Conference 2020 Conference Paper

Federated Patient Hashing

  • Jie Xu
  • Zhenxing Xu
  • Peter Walker
  • Fei Wang

Privacy concerns on sharing sensitive data across institutions are particularly paramount for the medical domain, which hinders the research and development of many applications, such as cohort construction for cross-institution observational studies and disease surveillance. Not only that, the large volume and heterogeneity of the patient data pose great challenges for retrieval and analysis. To address these challenges, in this paper, we propose a Federated Patient Hashing (FPH) framework, which collaboratively trains a retrieval model stored in a shared memory while keeping all the patientlevel information in local institutions. Specifically, the objective function is constructed by minimization of a similarity preserving loss and a heterogeneity digging loss, which preserves both inter-data and intra-data relationships. Then, by leveraging the concept of Bregman divergence, we implement optimization in a federated manner in both centralized and decentralized learning settings, without accessing the raw training data across institutions. In addition to this, we also analyze the convergence rate of the FPH framework. Extensive experiments on real-world clinical data set from critical care are provided to demonstrate the effectiveness of the proposed method on similar patient matching across institutions.

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AAAI Conference 2019 Conference Paper

Orthogonality-Promoting Dictionary Learning via Bayesian Inference

  • Lei Luo
  • Jie Xu
  • Cheng Deng
  • Heng Huang

Dictionary Learning (DL) plays a crucial role in numerous machine learning tasks. It targets at finding the dictionary over which the training set admits a maximally sparse representation. Most existing DL algorithms are based on solving an optimization problem, where the noise variance and sparsity level should be known as the prior knowledge. However, in practice applications, it is difficult to obtain these knowledge. Thus, non-parametric Bayesian DL has recently received much attention of researchers due to its adaptability and effectiveness. Although many hierarchical priors have been used to promote the sparsity of the representation in non-parametric Bayesian DL, the problem of redundancy for the dictionary is still overlooked, which greatly decreases the performance of sparse coding. To address this problem, this paper presents a novel robust dictionary learning framework via Bayesian inference. In particular, we employ the orthogonality-promoting regularization to mitigate correlations among dictionary atoms. Such a regularization, encouraging the dictionary atoms to be close to being orthogonal, can alleviate overfitting to training data and improve the discrimination of the model. Moreover, we impose Scale mixture of the Vector variate Gaussian (SMVG) distribution on the noise to capture its structure. A Regularized Expectation Maximization Algorithm is developed to estimate the posterior distribution of the representation and dictionary with orthogonality-promoting regularization. Numerical results show that our method can learn the dictionary with an accuracy better than existing methods, especially when the number of training signals is limited.

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AAAI Conference 2019 Conference Paper

Robust Metric Learning on Grassmann Manifolds with Generalization Guarantees

  • Lei Luo
  • Jie Xu
  • Cheng Deng
  • Heng Huang

In recent research, metric learning methods have attracted increasing interests in machine learning community and have been applied to many applications. However, the existing metric learning methods usually use a fixed L2-norm to measure the distance between pairwise data samples in the projection space, which cannot provide an effective mechanism to automatically remove the noise that exist in data samples. To address this issue, we propose a new robust formulation of metric learning. Our new model constructs a projection from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability, where the errors between sample points are considered as an MLE (maximum likelihood estimation)-like estimator. An efficient iteratively reweighted algorithm is derived to solve the proposed metric learning model. More importantly, we establish the generalization bounds for the proposed algorithm by utilizing the techniques of U-statistics. Experiments on six benchmark datasets clearly show that the proposed method achieves consistent improvements in discrimination accuracy, in comparison to state-of-the-art methods.

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NeurIPS Conference 2018 Conference Paper

Bilevel Distance Metric Learning for Robust Image Recognition

  • Jie Xu
  • Lei Luo
  • Cheng Deng
  • Heng Huang

Metric learning, aiming to learn a discriminative Mahalanobis distance matrix M that can effectively reflect the similarity between data samples, has been widely studied in various image recognition problems. Most of the existing metric learning methods input the features extracted directly from the original data in the preprocess phase. What's worse, these features usually take no consideration of the local geometrical structure of the data and the noise existed in the data, thus they may not be optimal for the subsequent metric learning task. In this paper, we integrate both feature extraction and metric learning into one joint optimization framework and propose a new bilevel distance metric learning model. Specifically, the lower level characterizes the intrinsic data structure using graph regularized sparse coefficients, while the upper level forces the data samples from the same class to be close to each other and pushes those from different classes far away. In addition, leveraging the KKT conditions and the alternating direction method (ADM), we derive an efficient algorithm to solve the proposed new model. Extensive experiments on various occluded datasets demonstrate the effectiveness and robustness of our method.

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NeurIPS Conference 2018 Conference Paper

Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward

  • Lixing Chen
  • Jie Xu
  • Zhuo Lu

In this paper, we study the stochastic contextual combinatorial multi-armed bandit (CC-MAB) framework that is tailored for volatile arms and submodular reward functions. CC-MAB inherits properties from both contextual bandit and combinatorial bandit: it aims to select a set of arms in each round based on the side information (a. k. a. context) associated with the arms. By ``volatile arms'', we mean that the available arms to select from in each round may change; and by ``submodular rewards'', we mean that the total reward achieved by selected arms is not a simple sum of individual rewards but demonstrates a feature of diminishing returns determined by the relations between selected arms (e. g. relevance and redundancy). Volatile arms and submodular rewards are often seen in many real-world applications, e. g. recommender systems and crowdsourcing, in which multi-armed bandit (MAB) based strategies are extensively applied. Although there exist works that investigate these issues separately based on standard MAB, jointly considering all these issues in a single MAB problem requires very different algorithm design and regret analysis. Our algorithm CC-MAB provides an online decision-making policy in a contextual and combinatorial bandit setting and effectively addresses the issues raised by volatile arms and submodular reward functions. The proposed algorithm is proved to achieve $O(cT^{\frac{2\alpha+D}{3\alpha + D}}\log(T))$ regret after a span of $T$ rounds. The performance of CC-MAB is evaluated by experiments conducted on a real-world crowdsourcing dataset, and the result shows that our algorithm outperforms the prior art.

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IJCAI Conference 2018 Conference Paper

Multi-Level Metric Learning via Smoothed Wasserstein Distance

  • Jie Xu
  • Lei Luo
  • Cheng Deng
  • Heng Huang

Traditional metric learning methods aim to learn a single Mahalanobis distance metric M, which, however, is not discriminative enough to characterize the complex and heterogeneous data. Besides, if the descriptors of the data are not strictly aligned, Mahalanobis distance would fail to exploit the relations among them. To tackle these problems, in this paper, we propose a multi-level metric learning method using a smoothed Wasserstein distance to characterize the errors between any two samples, where the ground distance is considered as a Mahalanobis distance. Since smoothed Wasserstein distance provides not only a distance value but also a flow-network indicating how the probability mass is optimally transported between the bins, it is very effective in comparing two samples whether they are aligned or not. In addition, to make full use of the global and local structures that exist in data features, we further model the commonalities between various classification through a shared distance matrix and the classification-specific idiosyncrasies with additional auxiliary distance matrices. An efficient algorithm is developed to solve the proposed new model. Experimental evaluations on four standard databases show that our method obviously outperforms other state-of-the-art methods.

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IJCAI Conference 2017 Conference Paper

Multi-Class Support Vector Machine via Maximizing Multi-Class Margins

  • Jie Xu
  • Xianglong Liu
  • Zhouyuan Huo
  • Cheng Deng
  • Feiping Nie
  • Heng Huang

Support Vector Machine (SVM) is originally proposed as a binary classification model, and it has already achieved great success in different applications. In reality, it is more often to solve a problem which has more than two classes. So, it is natural to extend SVM to a multi-class classifier. There have been many works proposed to construct a multi-class classifier based on binary SVM, such as one versus all strategy, one versus one strategy and Weston's multi-class SVM. One versus all strategy and one versus one strategy split the multi-class problem to multiple binary classification subproblems, and we need to train multiple binary classifiers. Weston's multi-class SVM is formed by ensuring risk constraints and imposing a specific regularization, like Frobenius norm. It is not derived by maximizing the margin between hyperplane and training data which is the motivation in SVM. In this paper, we propose a multi-class SVM model from the perspective of maximizing margin between training points and hyperplane, and analyze the relation between our model and other related methods. In the experiment, it shows that our model can get better or compared results when comparing with other related methods.

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IJCAI Conference 2017 Conference Paper

Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model

  • Jie Xu
  • Cheng Deng
  • Xinbo Gao
  • Dinggang Shen
  • Heng Huang

Alzheimer's disease (AD) is a neurodegenerative disorder with slow onset, which could result in the deterioration of the duration of persistent neurological dysfunction. How to identify the informative longitudinal phenotypic neuroimaging markers and predict cognitive measures are crucial to recognize AD at early stage. Many existing models related imaging measures to cognitive status using regression models, but they did not take full consideration of the interaction between cognitive scores. In this paper, we propose a robust low-rank structured sparse regression method (RLSR) to address this issue. The proposed model simultaneously selects effective features and learns the underlying structure between cognitive scores by utilizing novel mixed structured sparsity inducing norms and low-rank approximation. In addition, an efficient algorithm is derived to solve the proposed non-smooth objective function with proved convergence. Empirical studies on cognitive data of the ADNI cohort demonstrate the superior performance of the proposed method.

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AAAI Conference 2017 Conference Paper

Progressive Prediction of Student Performance in College Programs

  • Jie Xu
  • Yuli Han
  • Daniel Marcu
  • Mihaela van der Schaar

Accurately predicting students’ future performance based on their tracked academic records in college programs is crucial for effectively carrying out necessary pedagogical interventions to ensure students’ on-time graduation. Although there is a rich literature on predicting student performance in solving problems and studying courses using data-driven approaches, predicting student performance in completing college programs is much less studied and faces new challenges, mainly due to the diversity of courses selected by students and the requirement of continuous tracking and incorporation of students’ evolving progresses. In this paper, we develop a novel algorithm that enables progressive prediction of students’ performance by adapting ensemble learning techniques and utilizing education-specific domain knowledge. We prove its prediction performance guarantee and show its performance improvement against benchmark algorithms on a real-world student dataset from UCLA.

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AAMAS Conference 2016 Conference Paper

Relaxation for Constrained Decentralized Markov Decision Processes (Extended Abstract)

  • Jie Xu

This paper studies a class of decentralized multi-agent stochastic optimization problems. In these problems, each agent has only a partial view of the world state, and a partial control of the actions but must cooperatively maximize the long-term system reward. The state that an agent observe consists of two parts - a common public component and an agentspecific private component. Importantly, taking actions incurs costs and the actions that the agents can take are subject to an overall cost constraint in each interaction period. We formulate this problem as an infinite time horizon Decentralized Markov Decision Process (DEC-MDP) with resource constraints and develop efficient approximate algorithms that allow decentralized computation of the agent policy based on Lagrangian relaxation.

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JBHI Journal 2016 Journal Article

Using Contextual Learning to Improve Diagnostic Accuracy: Application in Breast Cancer Screening

  • Linqi Song
  • William Hsu
  • Jie Xu
  • Mihaela van der Schaar

Clinicians need to routinely make management decisions about patients who are at risk for a disease such as breast cancer. This paper presents a novel clinical decision support tool that is capable of helping physicians make diagnostic decisions. We apply this support system to improve the specificity of breast cancer screening and diagnosis. The system utilizes clinical context (e. g. , demographics, medical history) to minimize the false positive rates while avoiding false negatives. An online contextual learning algorithm is used to update the diagnostic strategy presented to the physicians over time. We analytically evaluate the diagnostic performance loss of the proposed algorithm, in which the true patient distribution is not known and needs to be learned, as compared with the optimal strategy where all information is assumed known, and prove that the false positive rate of the proposed learning algorithm asymptotically converges to the optimum. In addition, our algorithm also has the important merit that it can provide individualized confidence estimates about the accuracy of the diagnosis recommendation. Moreover, the relevancy of contextual features is assessed, enabling the approach to identify specific contextual features that provide the most value of information in reducing diagnostic errors. Experiments were conducted using patient data collected at a large academic medical center. Our proposed approach outperforms the current clinical practice by 36% in terms of false positive rate given a 2% false negative rate.

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AAMAS Conference 2012 Conference Paper

Token Economy for Online Exchange Systems

  • Jie Xu
  • William Zame
  • Mihaela van der Schaar

This paper studies the design of online exchange systems that are operated based on the exchange of tokens, a simple internal currency which provides indirect reciprocity among agents for cooperation. The emphasis is on how the protocol designer should choose a protocol - a supply of tokens and suggested strategies - to maximize service provision, taking into account that impatient agents will comply with the protocol if and only if it is in their interests to do so. The protocol is designed in such a way that it is robust to (small) errors in the designer’s knowledge of the system parameters. We prove that robust protocols have a simple pure threshold structure and there is a unique optimal supply of tokens that balances the token distribution in the population and achieves the optimal efficiency. In the meanwhile, we also emphasize that choosing the wrong token supply can result in an enormous efficiency loss. 1

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