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Xiaoguang Gao

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3 papers

1 author row

AAAI Conference 2026 Conference Paper

Robust Causal Discovery Under Imperfect Structural Constraints

Zidong Wang
Xi Lin
Chuchao He
Xiaoguang Gao

Robust causal discovery from observational data under imperfect prior knowledge remains a significant and largely unresolved challenge. Existing methods typically presuppose perfect priors or can only handle specific, pre-identified error types. And their performance degrades substantially when confronted with flawed constraints of unknown location and type. This decline arises because most of them rely on inflexible and biased thresholding strategies that may conflict with the data distribution. To overcome these limitations, we propose to harmonizes knowledge and data through prior alignment and conflict resolution. First, we assess the credibility of imperfect structural constraints through a surrogate model, which then guides a sparse penalization term measuring the loss between the learned and constrained adjacency matrices. We theoretically prove that, under ideal assumption, the knowledge-driven objective aligns with the data-driven objective. Furthermore, to resolve conflicts when this assumption is violated, we introduce a multi-task learning framework optimized via multi-gradient descent, jointly minimizing both objectives. Our proposed method is robust to both linear and nonlinear settings. Extensive experiments, conducted under diverse noise conditions and structural equation model types, demonstrate the effectiveness and efficiency of our method under imperfect structural constraints.

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IJCAI Conference 2025 Conference Paper

LLM-enhanced Score Function Evolution for Causal Structure Learning

Zidong Wang
Fei Liu
Qi Feng
Qingfu Zhang
Xiaoguang Gao

Causal structure learning (CSL) plays a pivotal role in causality and is often formulated as an optimization problem within score-and-search methods. Under the assumption of an infinite dataset and a predefined distribution, several well-established and consistent score functions have been shown to be both optimal and reliable for identifying ground-truth causal graphs. However, in practice, these idealized assumptions are often infeasible, which can result in CSL algorithms learning suboptimal structures. In this paper, we introduce L-SFE, a framework designed to automatically discover effective score functions by exploring the "score function space". L-SFE addresses this task from a bi-level optimization perspective. First, it leverages a Large Language Model (LLM) to interpret the characteristics of score functions and generate the corresponding code implementations. Next, L-SFE employs evolutionary algorithms along with carefully designed operators, to search for solutions with higher fitness. Additionally, we take the BIC as example and prove the consistency of the generated score functions. Experimental evaluations, conducted on discrete, continuous, and real datasets, demonstrate the high stability, generality and effectiveness of L-SFE.

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EAAI Journal 2025 Journal Article

Low-high frequency network for spatial–temporal traffic flow forecasting

Qi Feng
Bo Li
Xiaohan Liu
Xiaoguang Gao
Kaifang Wan

Traffic flow forecasting is crucial for smart city development. Existing methods primarily focus on spatial–temporal correlation learning but often overlook the distinct temporal characteristics of traffic flow. From the temporal perspective, traffic flow can be decomposed into low frequency components-representing periodic patterns inherent in the transportation system and high frequency components-reflecting short-term variations caused by external factors. However, current approaches tend to capture high frequency components while neglecting low frequency ones, which has resulted in a performance bottleneck. To address this problem, we propose a novel framework, named Low-High Frequency Network(LHFNet), for traffic flow forecasting. Our framework comprises three key components: a low frequency encoder, a high frequency encoder, and a frequency feature fusion block. The low frequency encoder employs bi-level routing attention as its core module. To enhance the stability of low frequency representations, patch embedding and patch merging operations are integrated, and the benefit of this integration is that the model complexity can be reduced while enabling longer input series. For the high frequency encoder, multilayer perceptrons are used with residual connections as the primary structure. Finally, the frequency feature fusion block dynamically integrates both frequency features through a gated selection mechanism. Extensive comparative experiments have been conducted on four real-world datasets and the results have demonstrated that LHFNet outperforms state-of-the-art models.

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