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Liang Yan

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5 papers

2 author rows

EAAI Journal 2025 Journal Article

A novel multi-fidelity sequential optimization method based on multi-level Gaussian process

Zecong Liu
Liang Yan
Xiaojun Duan
Yike Xiao
Bo Liu
Jiangtao Chen

Multi-fidelity optimization algorithms can efficiently find the optimum of the high-fidelity system with assistance of lower-fidelity and cheaper simulation(s). However, existing methods do not utilize this “assistance” sufficiently, where they generally tend to query high-fidelity systems during the optimization procedure. In this paper, we propose a novel sequential criterion based on multi-level Gaussian process (MLGP) and name it the multi-level expected improvement criterion (LEI). LEI is an extended version of the expected improvement criterion (EI) with a closed form, which integrates the correlation index, cost ratio, and constraint handling terms, hence determining both location and fidelity level of the next sample. Specifically, the correlation index is a function of the prediction error for each fidelity, which can reflect the metamodeling accuracy of low-fidelity and the desirable sampling site for high-fidelity system. We have proved that low-fidelity samples can improve the accuracy of MLGP modeling while the LEI criterion can improve the robustness and accuracy of optimization theoretically. Furthermore, the LEI criterion can easily be extended to multiple fidelity scenarios. The numerical results show that the proposed algorithm saves the sample size for high fidelity with higher optimization efficiency and stronger robustness.

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ECAI Conference 2025 Conference Paper

CRED-SQL: Enhancing Real-World Large Scale Database Text-to-SQL Parsing Through Cluster Retrieval and Execution Description

Shaoming Duan
Zirui Wang
Chuanyi Liu
Zhibin Zhu
Yuhao Zhang
Peiyi Han
Liang Yan
Zewu Peng

Recent advances in large language models (LLMs) have significantly improved the accuracy of Text-to-SQL systems. However, a critical challenge remains: the semantic mismatch between natural language questions (NLQs) and their corresponding SQL queries. This issue is exacerbated in large-scale databases, where semantically similar attributes hinder schema linking and semantic drift during SQL generation, ultimately reducing model accuracy. To address these challenges, we introduce CRED-SQL, a framework designed for large-scale databases that integrates Cluster Retrieval and Execution Description. CRED-SQL first performs cluster-based large-scale schema retrieval to pinpoint the tables and columns most relevant to a given NLQ, alleviating schema mismatch. It then introduces an intermediate natural language representation—Execution Description Language (EDL)—to bridge the gap between NLQs and SQL. This reformulation decomposes the task into two stages: Text-to-EDL and EDL-to-SQL, leveraging LLMs’ strong general reasoning capabilities while reducing semantic deviation. Extensive experiments on two large-scale, cross-domain benchmarks—SpiderUnion and BirdUnion—demonstrate that CRED-SQL achieves new state-of-the-art (SOTA) performance, validating its effectiveness and scalability. Our code is available at https: //github. com/smduan/CRED-SQL. git

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NeurIPS Conference 2025 Conference Paper

Geometric Imbalance in Semi-Supervised Node Classification

Liang Yan
Shengzhong Zhang
Bisheng Li
Menglin Yang
Chen Yang
Min Zhou
Weiyang Ding
Yutong Xie

Class imbalance in graph data presents a significant challenge for effective node classification, particularly in semi-supervised scenarios. In this work, we formally introduce the concept of geometric imbalance, which captures how message passing on class-imbalanced graphs leads to geometric ambiguity among minority-class nodes in the riemannian manifold embedding space. We provide a rigorous theoretical analysis of geometric imbalance on the riemannian manifold and propose a unified framework that explicitly mitigates it through pseudo-label alignment, node reordering, and ambiguity filtering. Extensive experiments on diverse benchmarks show that our approach consistently outperforms existing methods, especially under severe class imbalance. Our findings offer new theoretical insights and practical tools for robust semi-supervised node classification.

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EAAI Journal 2021 Journal Article

Identifying the module structure of swarms using a new framework of network-based time series clustering

Kongjing Gu
Ziyang Mao
Xiaojun Duan
Guanlin Wu
Liang Yan

Swarm is a collective motion phenomenon whose dynamic mechanism and cooperation structure could be identified based on observations. Unmanned Aerial Vehicles (UAV) is a special artificial swarm with unique rules and structures. Therefore, corresponding identification methods need to be developed. One critical identification problem is distinguishing the swarm’s cooperation structure, which is usually clustered and grouped to achieve stability of behaviors and low communication cost. This paper proposes a framework of Overlay Network Integrated Time series clustering (ONIT) to identify the UAV swarm structures based on trajectories. The framework consists of Snapshot, Net Growth and Net Split. It can fuse with most distance functions in time series clustering, achieving high accuracy, update ability, and fault tolerance with various datasets. We create point-based and sliding window-based snapshots, allowing the framework compatible with more methods. In particular, the Dynamic Time Wrapping (DTW) correspondence in point-based snapshots shows the high scalability of the framework, and the Euclidean Distance (ED) correspondence shows that the framework can still significantly improve the accuracy while maintaining the simplicity of calculation. The test results show that the fused ONIT-clustering algorithms, especially the point-based ones, outperform original time series clustering methods separately in simulation datasets of UAV swarms and UCR repository by 28% and 27%. In summary, the proposed framework is a flexible and scalable time series clustering method that can solve various time series clustering problems especially the trajectory clustering of the UAV swarm and has great potential for general time series analysis.

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IROS Conference 2011 Conference Paper

Trajectory planning and current control optimization of three degree-of-freedom spherical actuator

Liang Zhang
Weihai Chen
Liang Yan
Jingmeng Liu

The study in this paper covers torque modeling, trajectory planning and optimization control of current input of spherical actuators, in which the latter two are the major contributions. Trajectory planning is an effective way to improve the smoothness and stability of rotor motions. A novel three-dimensional (3D) orientation representation method based on manifold of S 2 is proposed to facilitate the trajectory planning of rotor. Current redundancy of spherical actuator is analyzed in detail, and optimization algorithm of current input is developed to improve the power efficiency and the fault tolerance capability of system. Simulation is then carried out to validate the proposed method and algorithm in this study. The simulation results indicate that by using the trajectory planning, the given torque values and the torque model, optimal current could be obtained to drive the rotor to achieve desired motions.

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