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Weidong Liu

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13 papers

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AAMAS Conference 2026 Conference Paper

CQGen-MAS: A Multi-Agent System for Competency Questions Generation from Ontology

Fei Du
Huilong An
Feng Li
Li Chen
Weidong Liu

Competency questions (CQs) delineate an ontology’s intended scope and use, yet many published ontologies are released without CQs, hindering interpretation and reuse. We present CQGen-MAS, a multi-agent system that automatically generates CQs from an existing ontology. We evaluate CQGen-MAS on a diverse set of ontologies and compare it with baseline approaches. Experimental results show that CQGen-MAS consistently performs favorably across diverse ontologies, indicating improved coverage and stronger semantic alignment with their conceptual scope.

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EAAI Journal 2025 Journal Article

A novel approach for understanding the parking demand for internal access roads in hub car parks

Qianyi Hu
Weidong Liu
Chenyu Yan
Chu Zhang
Jun Chen
Changyin Dong

The hubs serve as pivotal nodes within urban transport networks, and the car parks associated with these hubs constitute an integral component. The prevalent practice in China of utilizing internal access roads within car parks for passenger pick-up by online car-hailing vehicles has engendered a novel category of parking demand. However, this development presents a significant challenge to the efficient operation of hub car parks. A thorough analysis and comprehension of the parking demand for internal access roads is essential in devising suitable management strategies to enhance parking efficiency. Initially, this study categorizes the parking demand for internal access roads into three distinct groups, applying parking duration and the number of entries as classification criteria. Subsequently, considering the number of train frequencies, the categorized parking demands are forecasted independently utilizing the Long Short-Term Memory (LSTM) model, complemented by the Shapley Additive exPlanations (SHAP) method for model interpretation. Ultimately, Vector Autoregression (VAR) is employed to investigate the interaction mechanism between the parking demand for parking spaces and the parking demand for internal access roads. The findings indicate that optimal predictive performance is attained when employing a time interval of 15 min and an input step size of 4. Competition between the parking demand for internal access roads and the parking demand for parking spaces primarily occurs within respective categories, underscoring the need for external interventions when these demands are imbalanced.

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EAAI Journal 2025 Journal Article

Sequential state estimation based target tracking algorithm for unmanned underwater vehicle target grasping

Yanli Li
Weidong Liu
Wenbo Zhang
Le Li

To address the challenges of underwater target tracking in dynamic and turbid environments, this paper proposes a Sequential State Estimation-based Tracking (SSET) algorithm that integrates temporal and spatial information for robust performance. The SSET algorithm comprises three key components: a Kalman filter-based sequential state estimation module to predict target motion and establish temporal correlations, a score head module to evaluate template reliability and optimize predictions, and a mixed-sequential-state transformer (MSST) to fuse triplet features for spatio-temporal correlation. Evaluations on open-air and underwater benchmarks demonstrate SSET’s superiority: it achieves 91. 7% precision and 69. 8% success rate on terrestrial datasets, outperforming state-of-the-art methods by 0. 6% and 0. 2%, respectively. In underwater scenarios, SSET attains 56. 5% precision and 55. 8% success rate, with improvements in occlusion and low-resolution conditions. Underwater grasping experiments further validate its practicality, achieving a high success rate in controlled environments.

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TIST Journal 2025 Journal Article

The Evaluation Framework and Benchmark for Large Language Models in the Government Affairs Domain

Shuo Liu
Lin Zhang
Weidong Liu
Jianfeng Zhang
Donghui Gao
Xiaofeng Jia

The rapid evolution of AI has driven advancements across numerous sectors. In the domain of government affairs, large language models (LLMs) hold significant potential for applications such as policy analysis, data processing, and decision support. However, their adoption in government settings faces considerable challenges, including data accessibility issues, the absence of standardized evaluation criteria, and concerns regarding model accuracy, reliability, and security. To address these challenges, we propose a comprehensive evaluation framework specifically designed for LLMs in government affairs. Built on modular principles, this framework ensures adaptability across various industries. Additionally, we introduce the Multi-Scenario Government Affairs Benchmark (MSGABench 1 ) dataset, a Chinese-language dataset specifically crafted to meet the practical needs of government professionals. Employing the proposed framework and the MSGA dataset, we conducted an empirical evaluation of 15 prominent LLMs, revealing critical insights: (1) Performance: Many models demonstrated low accuracy and reliability, particularly under minor input variations, with some dropping below 35% accuracy, whereas GPT-4 achieved above 95% reliability; (2) Security and Compliance: Significant concerns were identified, including privacy vulnerabilities, legal compliance risks, and persistent biases, which may hinder secure deployments in government contexts; (3) Task Avoidance: Certain models exhibited excessive caution, often avoiding responses to basic tasks like document classification and government-related inquiries, which restricts their usability. These findings highlight essential limitations and opportunities for improvement, contributing to the safe and effective application of LLMs in the government sector.

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JMLR Journal 2024 Journal Article

Distributed Estimation on Semi-Supervised Generalized Linear Model

Jiyuan Tu
Weidong Liu
Xiaojun Mao

Semi-supervised learning is devoted to using unlabeled data to improve the performance of machine learning algorithms. In this paper, we study the semi-supervised generalized linear model (GLM) in the distributed setup. In the cases of single or multiple machines containing unlabeled data, we propose two distributed semi-supervised algorithms based on the distributed approximate Newton method. When the labeled local sample size is small, our algorithms still give a consistent estimation, while fully supervised methods fail to converge. Moreover, we theoretically prove that the convergence rate is greatly improved when sufficient unlabeled data exists. Therefore, the proposed method requires much fewer rounds of communications to achieve the optimal rate than its fully-supervised counterpart. In the case of the linear model, we prove the rate lower bound after one round of communication, which shows that rate improvement is essential. Finally, several simulation analyses and real data studies are provided to demonstrate the effectiveness of our method. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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EAAI Journal 2024 Journal Article

Intelligent mining methodology of product field failure data by fusing deep learning and association rules for after-sales service text

Yan Liu
Shijie Hu
Haichun Zhang
Qiuxian Dong
Weidong Liu

The after-sales service text contains wealthy field failure data, which can be used to estimate the field reliability of product and its components. However, human-based data mining suffers from low efficiency and unstable quality. This study proposes an innovative intelligent mining methodology that combines the product failure dictionary (PFD), gate recurrent unit (GRU) and association rules (AR) to solve the problem. Firstly, the text is studied, and the segmented mining process is developed. Secondly, the PFD is edited according to the analysis results of a large product failure sample, and combined with GRU to construct the PFD-GRU efficient mining model. Thirdly, based on the failure determination criteria and the dependency syntax analysis results of the text, the AR-based mining method is formulated for re-mining the text that does not satisfy the determination threshold of the PFD-GRU model to establish the PFD-GRU-AR intelligent mining methodology. The case study results of the after-sales service text mining for four types of air-conditioner show that the segmented mining pattern can obtain better quality mining results than the one-stage mining pattern, and the P, R, and F 1 of the PFD-GRU model have increased by at least 1. 48%, 1. 72% and 1. 6% respectively compared to the corresponding indicator values of the GRU model. With the increase of the threshold value, the quality and robustness are improved and the mining results tend to be stable. As threshold value is 0. 95, the PFD-GRU-AR methodology obtains minimum value for P, R, and F 1 as 98. 54%, 99. 09% and 98. 81% respectively.

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NeurIPS Conference 2024 Conference Paper

OneBit: Towards Extremely Low-bit Large Language Models

Yuzhuang Xu
Xu Han
Zonghan Yang
Shuo Wang
Qingfu Zhu
Zhiyuan Liu
Weidong Liu
Wanxiang Che

Model quantification uses low bit-width values to represent the weight matrices of existing models to be quantized, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs. However, current quantization methods suffer severe performance degradation when the bit-width is extremely reduced, and thus focus on utilizing 4-bit or 8-bit values to quantize models. This paper boldly quantizes the weight matrices of LLMs to 1-bit, paving the way for the extremely low bit-width deployment of LLMs. For this target, we introduce a 1-bit model compressing framework named OneBit, including a novel 1-bit parameter representation method to better quantize LLMs as well as an effective parameter initialization method based on matrix decomposition to improve the convergence speed of the quantization framework. Sufficient experimental results indicate that OneBit achieves good performance (at least 81% of the non-quantized performance on LLaMA models) with robust training processes when only using 1-bit weight matrices.

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NeurIPS Conference 2024 Conference Paper

ST$_k$: A Scalable Module for Solving Top-k Problems

Hanchen Xia
Weidong Liu
Xiaojun Mao

The cost of ranking becomes significant in the new stage of deep learning. We propose ST$_k$, a fully differentiable module with a single trainable parameter, designed to solve the Top-k problem without requiring additional time or GPU memory. Due to its fully differentiable nature, ST$_k$ can be embedded end-to-end into neural networks and optimize the Top-k problems within a unified computational graph. We apply ST$_k$ to the Average Top-k Loss (AT$_k$), which inherently faces a Top-k problem. The proposed ST$_k$ Loss outperforms AT$_k$ Loss and achieves the best average performance on multiple benchmarks, with the lowest standard deviation. With the assistance of ST$_k$ Loss, we surpass the state-of-the-art (SOTA) on both CIFAR-100-LT and Places-LT leaderboards.

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EAAI Journal 2022 Journal Article

Failure mode risk assessment methodology for controlling multi-uncertainties in the evaluation process

Yan Liu
Bingsong Chen
Qiuxian Dong
Weidong Liu
Wenbin Nie
Chao Yang

The failure mode risk evaluation results of FMEA are affected by multi-uncertainties. This paper proposes a risk evaluation methodology for controlling multi-uncertainties in the assessment process. First, the fuzzy confidence interval number (FCIN) evaluation model is provided to control the uncertainty in assessing the severity (S), occurrence (O), and detectability (D). Then, the FCINs are converted into generalized trapezoidal fuzzy numbers (GTrFNs), and the GTrFNs’ scalar characteristic distances modified by the non-membership are used as the evaluation results of S, O, D and their synthesizer or risk priority number (RPN) to control the risk evaluation model uncertainty. Furthermore, the evaluation parameter value criteria of S, O, D are formulated based on the sensitivity analysis results, more precise than the general value guidelines introduced by industrial FMEA standards. The case study results show that the proposed methodology can significantly improve the risk assessment results and the risk discrimination of failure modes.

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JMLR Journal 2021 Journal Article

Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference

Jiyuan Tu
Weidong Liu
Xiaojun Mao
Xi Chen

This paper develops an efficient distributed inference algorithm, which is robust against a moderate fraction of Byzantine nodes, namely arbitrary and possibly adversarial machines in a distributed learning system. In robust statistics, the median-of-means (MOM) has been a popular approach to hedge against Byzantine failures due to its ease of implementation and computational efficiency. However, the MOM estimator has the shortcoming in terms of statistical efficiency. The first main contribution of the paper is to propose a variance reduced median-of-means (VRMOM) estimator, which improves the statistical efficiency over the vanilla MOM estimator and is computationally as efficient as the MOM. Based on the proposed VRMOM estimator, we develop a general distributed inference algorithm that is robust against Byzantine failures. Theoretically, our distributed algorithm achieves a fast convergence rate with only a constant number of rounds of communications. We also provide the asymptotic normality result for the purpose of statistical inference. To the best of our knowledge, this is the first normality result in the setting of Byzantine-robust distributed learning. The simulation results are also presented to illustrate the effectiveness of our method. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

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IJCAI Conference 2020 Conference Paper

Bayesian Decision Process for Budget-efficient Crowdsourced Clustering

Xiaozhou Wang
Xi Chen
Qihang Lin
Weidong Liu

The performance of clustering depends on an appropriately defined similarity between two items. When the similarity is measured based on human perception, human workers are often employed to estimate a similarity score between items in order to support clustering, leading to a procedure called crowdsourced clustering. Assuming a monetary reward is paid to a worker for each similarity score and assuming the similarities between pairs and workers' reliability have a large diversity, when the budget is limited, it is critical to wisely assign pairs of items to different workers to optimize the clustering result. We model this budget allocation problem as a Markov decision process where item pairs are dynamically assigned to workers based on the historical similarity scores they provided. We propose an optimistic knowledge gradient policy where the assignment of items in each stage is based on the minimum-weight K-cut defined on a similarity graph. We provide simulation studies and real data analysis to demonstrate the performance of the proposed method.

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JMLR Journal 2020 Journal Article

Distributed High-dimensional Regression Under a Quantile Loss Function

Xi Chen
Weidong Liu
Xiaojun Mao
Zhuoyi Yang

This paper studies distributed estimation and support recovery for high-dimensional linear regression model with heavy-tailed noise. To deal with heavy-tailed noise whose variance can be infinite, we adopt the quantile regression loss function instead of the commonly used squared loss. However, the non-smooth quantile loss poses new challenges to high-dimensional distributed estimation in both computation and theoretical development. To address the challenge, we transform the response variable and establish a new connection between quantile regression and ordinary linear regression. Then, we provide a distributed estimator that is both computationally and communicationally efficient, where only the gradient information is communicated at each iteration. Theoretically, we show that, after a constant number of iterations, the proposed estimator achieves a near-oracle convergence rate without any restriction on the number of machines. Moreover, we establish the theoretical guarantee for the support recovery. The simulation analysis is provided to demonstrate the effectiveness of our method. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

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JMLR Journal 2019 Journal Article

Distributed Inference for Linear Support Vector Machine

Xiaozhou Wang
Zhuoyi Yang
Xi Chen
Weidong Liu

The growing size of modern data brings many new challenges to existing statistical inference methodologies and theories, and calls for the development of distributed inferential approaches. This paper studies distributed inference for linear support vector machine (SVM) for the binary classification task. Despite a vast literature on SVM, much less is known about the inferential properties of SVM, especially in a distributed setting. In this paper, we propose a multi-round distributed linear-type (MDL) estimator for conducting inference for linear SVM. The proposed estimator is computationally efficient. In particular, it only requires an initial SVM estimator and then successively refines the estimator by solving simple weighted least squares problem. Theoretically, we establish the Bahadur representation of the estimator. Based on the representation, the asymptotic normality is further derived, which shows that the MDL estimator achieves the optimal statistical efficiency, i.e., the same efficiency as the classical linear SVM applying to the entire data set in a single machine setup. Moreover, our asymptotic result avoids the condition on the number of machines or data batches, which is commonly assumed in distributed estimation literature, and allows the case of diverging dimension. We provide simulation studies to demonstrate the performance of the proposed MDL estimator. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

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