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Huanhuan Chen

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20 papers

1 author row

AAAI Conference 2026 Conference Paper

Counterfactual-Driven Zero-Shot Classifier Expansion

Xiangyu Wang
Yanze Gao
Changxin Rong
Lyuzhou Chen
Derui Lyu
Xiren Zhou
Taiyu Ban
Huanhuan Chen

Zero-shot classifier expansion aims to adapt existing model to new, unseen classes. It utilizes class attributes or textual descriptions to learn a mapping from the semantic space to the classifier's weight space, without requiring new visual training data. However, the learning process for this mapping relies solely on correlating semantic patterns with their corresponding classifier weights and lacks explicit modeling of inter-class differences. This makes it difficult for the model to capture the critical discriminative features required to define classification boundaries. To overcome this limitation, we reframe the problem from a causal perspective and introduce a novel framework driven by counterfactuals. Our method first generates factual descriptions alongside corresponding inter-class counterfactuals to pinpoint the causal attributes essential for classification, then refines these representations via a mutual purification process, and finally leverages a novel separation loss to explicitly push the factual and counterfactual classifier weights apart. This strategy forces the model to forge clearer and more discriminative classification boundaries, achieving more accurate and robust classification. Extensive experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

Fault Diagnosis of Irregular Sequences by Adjoint Learning in Continuous-Time Model Space

Xiren Zhou
Chuyang Wei
Ao Chen
Shikang Liu
Xiangyu Wang
Huanhuan Chen

Fault Diagnosis (FD) on sequential data suffers from irregular sampling (with missing values), limited training data, and varying underlying environments. In response, this paper proposes FD by adjoint learning in continuous-time model space. Model-Space Learning employs well-fitted models that capture data's dynamics (i.e., changing information) as more stable and concise representations of the original data. The Continuous-Time Reservoir Computing Network (CT-Res) is first introduced, which embeds Ordinary Differential Equation (ODE) within the reservoir-based hidden layer to govern continuous-time hidden-state evolution, naturally handling irregular sampling without relying on fixed time steps and effectively capturing intrinsic data dynamics. By fitting each sequence via CT-Res and representing it with the fitted model, the original sequences are mapped from the data space into the continuous-time model space. We further develop an adjoint learning strategy by incorporating a discrete-time "adjoint Echo State Network (ESN)" that shares structure and parameters with CT-Res, thus enabling efficient training by bypassing the computationally intensive ODE solver, with joint optimization of fitting accuracy and class discrimination in the model space. Experiments on multiple FD benchmarks highlight the effectiveness and efficiency of our study, particularly with missing values and scarce training data.

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AAAI Conference 2026 Conference Paper

SVGL: Scale-Variable Graph Learning in Model Space for Multivariate Time Series Classification

Shikang Liu
Ziyu Tang
Xiren Zhou
Huanhuan Chen

Multivariate time series classification (MTSC) has broad applications in numerous domains. Existing MTSC methods typically focus on either temporal dynamics or variable interactions of the data, often overlooking cross-scale couplings among different variables. To bridge this gap, we propose Scale-Variable Graph Learning (SVGL), a novel framework that effectively captures data-inherent scale-variable interactions for MTSC. SVGL begins with spectral analysis to adaptively identify key periodic scales for each variable. A period-aware reservoir computing network is then incorporated to fit the variable at these scales, encoding the sequential and periodic dynamics into multi-scale dynamic representations. Subsequently, we construct a scale-variable graph to model interactions of the encoded temporal dynamics, where nodes represent scale-variable pairs and edges denote their correlations. After sparsely initializing the graph via nearest neighbors, a parallel graph learning architecture is integrated in SVGL, combining global graph convolutional and sample-specific graph attention to aggregate effective features for classification. Extensive experiments on 30 UEA datasets demonstrate that SVGL outperforms state-of-the-art baselines in accuracy and maintains low training overhead.

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AAAI Conference 2025 Conference Paper

Efficient Anomaly Detection of Irregular Sequences in Ct-Echo Model Space

Ao Chen
Xiren Zhou
Huanhuan Chen

Efficient anomaly detection of irregular sequences, especially those characterized by non-uniform sampling from discontinuous operations or unreliable sensors, presents challenges across various fields. In response, this paper introduces irregular-sequence classification in ''Ct-Echo Model Space''. A novel Continuous-time Echo Network (Ct-Echo) is proposed to fit irregular sequences, efficiently capturing their inherent dynamic characteristics. Ct-Echo utilizes the ''Echo'' mechanism, where history information influences the current state and diminishes over time, and employs Ordinary Differential Equation (ODE) to construct continuous-time transition of hidden states. Each sequence is individually fitted via Ct-Echo to derive a readout model. These fitted models, capturing the dynamic characteristics of the original data, serve as representations of the corresponding sequences, thus mapping the original data from the data space to the Ct-Echo model space. Anomaly detection is further performed in this model space, evaluating differences between models rather than directly on the original sequences. Our method enhances real-time processing and lessens reliance on the amount of labeled training data, as demonstrated by experimental studies.

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IJCAI Conference 2025 Conference Paper

Enhancing Counterfactual Estimation: A Focus on Temporal Treatments

Xin Wang
Shengfei Lyu
Kangyang Luo
Lishan Yang
Huanhuan Chen
Chunyan Miao

In the medical field, treatment sequences significantly influence future outcomes through complex temporal interactions. Therefore, highlighting the role of temporal treatments within the model is crucial for accurate counterfactual estimation, which is often overlooked in current methods. To address this, we employ Koopman theory, known for its capability to model complex dynamic systems, and introduce a novel model named the Counterfactual Temporal Dynamics Network via Neural Koopman Operators (CTD-NKO). This model utilizes Koopman operators to encapsulate sequential treatment data, aiming to capture the causal dynamics within the system induced by temporal interactions between treatments. Moreover, CTD-NKO implements a weighting strategy that aligns joint and marginal distributions of the system state and the current treatment to mitigate time-varying confounding bias. This deviates from the balanced representation strategy employed by existing methods, as we demonstrate that such a strategy may suffer from the potential information loss of historical treatments. These designs allow CTD-NKO to exploit treatment information more thoroughly and effectively, resulting in superior performance on both synthetic and real-world datasets.

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IJCAI Conference 2025 Conference Paper

Expanding the Category of Classifiers with LLM Supervision

Derui Lyu
Xiangyu Wang
Taiyu Ban
Lyuzhou Chen
Xiren Zhou
Huanhuan Chen

Zero-shot learning has shown significant potential for creating cost-effective and flexible systems to expand classifiers to new categories. However, existing methods still rely on manually created attributes designed by domain experts. Motivated by the widespread success of large language models (LLMs), we introduce an LLM-driven framework for class-incremental learning that removes the need for human intervention, termed Classifier Expansion with Multi-vIew LLM knowledge (CEMIL). In CEMIL, an LLM agent autonomously generates detailed textual multi-view descriptions for unseen classes, offering richer and more flexible class representations than traditional expert-constructed vectorized attributes. These LLM-derived textual descriptions are integrated through a contextual filtering attention mechanism to produce discriminative class embeddings. Subsequently, a weight injection module maps the class embeddings to classifier weights, enabling seamless expansion to new classes. Experimental results show that CEMIL outperforms existing methods using expert-constructed attributes, demonstrating its effectiveness for fully automated classifier expansion without human participation.

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IJCAI Conference 2025 Conference Paper

Fault Diagnosis in REDNet Model Space

Xiren Zhou
Ziyu Tang
Shikang Liu
Ao Chen
Xiangyu Wang
Huanhuan Chen

Fault Diagnosis (FD) in time-varying data presents considerations such as limited training data, intra- and inter-dimensional correlations, and constraints of training time. In response, this paper introduces FD in the Reservoir-Embedded-Directional Network (REDNet) model space. Model-oriented methods utilize well-fitted networks or functions, denoted as "models" that capture data's changing information, as more stable and parsimonious representations of the data. Our approach employs REDNet for data fitting, wherein multiple reservoirs are organized along intrinsic correlation directions to establish intra- and inter-dimensional dependencies, thereby capturing multi-directional dynamics in high-dimensional data. Representing each data instance with an independently fitted REDNet model maps these instances into a class-separable REDNet model space, where FD could be performed on the models rather than the original data. Concentrating on the data-intrinsic dynamics, our method achieves rapid training speeds, and maintains robust performance even with minimal training data. Experiments on several datasets demonstrate its effectiveness.

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NeurIPS Conference 2025 Conference Paper

Pattern-Guided Adaptive Prior for Structure Learning

Lyuzhou Chen
Yijia Sun
Yanze Gao
Xiangyu Wang
Derui Lyu
Taiyu Ban
Xin Wang
Xiren Zhou

Learning the causality between variables, known as DAG structure learning, is critical yet challenging due to issues such as insufficient data and noise. While prior knowledge can improve the learning process and refine the DAG structure, incorporating prior knowledge is not without pitfalls. In particular, we find that the gap between the imprecise prior knowledge and the exact weights modeled by existing methods may result in deviation in edge weights. Such deviation can subsequently cause significant inaccuracies when learning the DAG structure. This paper addresses this challenge by providing a theoretical analysis of the impact of deviation in edge weights during the optimization process of structure learning. We identify two special graph patterns that arise due to the deviation and show that their occurrence increases as the degree of deviation grows. Building on this analysis, we propose the Pattern-Guided Adaptive Prior (PGAP) framework. PGAP detects these patterns as structural signals during optimization and adaptively adjusts the structure learning process to counteract the identified weight deviation, thereby improving the integration of prior knowledge. Experiments verify the effectiveness and robustness of the proposed method.

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IJCAI Conference 2025 Conference Paper

Underground Diagnosis in 3D GPR Data by Learning in CuCoRes Model Space

Xiren Zhou
Shikang Liu
Xinyu Yan
Xiangyu Wang
Huanhuan Chen

Ground Penetrating Radar (GPR) provides detailed subterranean insights. Nevertheless, underground diagnosis via GPR is hindered by the fact that training data typically contain only normal samples, along with the complexity of GPR data’s wave-collection characteristics. This paper proposes subsurface anomaly detection within the Cubic Correlation Reservoir Network (CuCoRes) model space. CuCoRes incorporates three reservoirs with spatial correlation adjustment in each direction to adequately and accurately capture multi-directional dynamics (i. e. , changing information) within GPR data. Fitting GPR data with CuCoRes and representing data with fitted models, the original GPR data is mapped into a category-discriminative CuCoRes model space, where anomalies could be efficiently identified and categorized based on model dissimilarities. Our approach leverages only limited normal GPR data, easily accessible, to support subsequent anomaly detection and categorization, enhancing its applicability in practical scenarios. Experiments on real-world data demonstrate its effectiveness, outperforming state-of-the-art.

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AAAI Conference 2024 Conference Paper

Audio Scanning Network: Bridging Time and Frequency Domains for Audio Classification

Liangwei Chen
Xiren Zhou
Huanhuan Chen

With the rapid growth of audio data, there's a pressing need for automatic audio classification. As a type of time-series data, audio exhibits waveform fluctuations in both the time and frequency domains that evolve over time, with similar instances sharing consistent patterns. This study introduces the Audio Scanning Network (ASNet), designed to leverage abundant information for achieving stable and effective audio classification. ASNet captures real-time changes in audio waveforms across both time and frequency domains through reservoir computing, supported by Reservoir Kernel Canonical Correlation Analysis (RKCCA) to explore correlations between time-domain and frequency-domain waveform fluctuations. This innovative approach empowers ASNet to comprehensively capture the changes and inherent correlations within the audio waveform, and without the need for time-consuming iterative training. Instead of converting audio into spectrograms, ASNet directly utilizes audio feature sequences to uncover associations between time and frequency fluctuations. Experiments on environmental sound and music genre classification tasks demonstrate ASNet's comparable performance to state-of-the-art methods.

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NeurIPS Conference 2024 Conference Paper

Differentiable Structure Learning with Partial Orders

Taiyu Ban
Lyuzhou Chen
Xiangyu Wang
Xin Wang
Derui Lyu
Huanhuan Chen

Differentiable structure learning is a novel line of causal discovery research that transforms the combinatorial optimization of structural models into a continuous optimization problem. However, the field has lacked feasible methods to integrate partial order constraints, a critical prior information typically used in real-world scenarios, into the differentiable structure learning framework. The main difficulty lies in adapting these constraints, typically suited for the space of total orderings, to the continuous optimization context of structure learning in the graph space. To bridge this gap, this paper formalizes a set of equivalent constraints that map partial orders onto graph spaces and introduces a plug-and-play module for their efficient application. This module preserves the equivalent effect of partial order constraints in the graph space, backed by theoretical validations of correctness and completeness. It significantly enhances the quality of recovered structures while maintaining good efficiency, which learns better structures using 90\% fewer samples than the data-based method on a real-world dataset. This result, together with a comprehensive evaluation on synthetic cases, demonstrates our method's ability to effectively improve differentiable structure learning with partial orders.

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IJCAI Conference 2024 Conference Paper

Learning in CubeRes Model Space for Anomaly Detection in 3D GPR Data

Xiren Zhou
Shikang Liu
Ao Chen
Huanhuan Chen

Three-dimensional Ground Penetrating Radar (3D GPR) data offer comprehensive views of the subsurface, yet identifying and classifying underground anomalies from this data is challenging due to limitations like scarce training data and variable underground environments. In response, we introduce learning in the Cube Reservoir Network (CubeRes) model space for efficient and accurate subsurface anomaly detection. CubeRes, incorporating three reservoirs, captures the dynamics in both horizontal and vertical directions inherent in the 3D GPR data. Fitting the data with CubeRes, representing the data with the compact fitted model, and measuring the difference between models by a proposed parameterized model metric, the original data is transformed from the data space to the CubeRes model space. Subsequently, we introduce an optimization strategy in this model space, aimed at bolstering fitting accuracy and improving category discrimination. This enhancement facilitates a more nuanced differentiation of dynamics across various GPR data categories, thereby enabling effective classification on the models rather than the original data. Experiments on real-world data validate our method's effectiveness and superiority, particularly in data-limited scenarios.

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AAAI Conference 2023 Conference Paper

Practical Markov Boundary Learning without Strong Assumptions

Xingyu Wu
Bingbing Jiang
Tianhao Wu
Huanhuan Chen

Theoretically, the Markov boundary (MB) is the optimal solution for feature selection. However, existing MB learning algorithms often fail to identify some critical features in real-world feature selection tasks, mainly because the strict assumptions of existing algorithms, on either data distribution, variable types, or correctness of criteria, cannot be satisfied in application scenarios. This paper takes further steps toward opening the door to real-world applications for MB. We contribute in particular to a practical MB learning strategy, which can maintain feasibility and effectiveness in real-world data where variables can be numerical or categorical with linear or nonlinear, pairwise or multivariate relationships. Specifically, the equivalence between MB and the minimal conditional covariance operator (CCO) is investigated, which inspires us to design the objective function based on the predictability evaluation of the mapping variables in a reproducing kernel Hilbert space. Based on this, a kernel MB learning algorithm is proposed, where nonlinear multivariate dependence could be considered without extra requirements on data distribution and variable types. Extensive experiments demonstrate the efficacy of these contributions.

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JBHI Journal 2022 Journal Article

Dynamic Link Prediction for Discovery of New Impactful COVID-19 Research Approaches

Xiangyu Wang
Yuan Li
Taiyu Ban
Jiarun Zhu
Lyuzhou Chen
Muhammad Usman
Xin Wang
Huanhuan Chen

In fighting the COVID-19 pandemic, the main challenges include the lack of prior research and the urgency to find effective solutions. It is essential to accurately and rapidly summarize the relevant research work and explore potential solutions for diagnosis, treatment and prevention of COVID-19. It is a daunting task to summarize the numerous existing research works and to assess their effectiveness. This paper explores the discovery of new COVID-19 research approaches based on dynamic link prediction, which analyze the dynamic topological network of keywords to predict possible connections of research concepts. A dynamic link prediction method based on multi-granularity feature fusion is proposed. Firstly, a multi-granularity temporal feature fusion method is adopted to extract the temporal evolution of different order subgraphs. Secondly, a hierarchical feature weighting method is proposed to emphasize actively evolving nodes. Thirdly, a semantic repetition sampling mechanism is designed to avoid the negative effect of semantically equivalent medical entities on the real structure of the graph, and to capture the real topological structure features. Experiments are performed on the COVID-19 Open Research Dataset to assess the performance of the model. The results show that the proposed model performs significantly better than existing state-of-the-art models, thereby confirming the effectiveness of the proposed method for the discovery of new COVID-19 research approaches.

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NeurIPS Conference 2022 Conference Paper

Generalization Bounds for Estimating Causal Effects of Continuous Treatments

Xin Wang
Shengfei Lyu
Xingyu Wu
Tianhao Wu
Huanhuan Chen

We focus on estimating causal effects of continuous treatments (e. g. , dosage in medicine), also known as dose-response function. Existing methods in causal inference for continuous treatments using neural networks are effective and to some extent reduce selection bias, which is introduced by non-randomized treatments among individuals and might lead to covariate imbalance and thus unreliable inference. To theoretically support the alleviation of selection bias in the setting of continuous treatments, we exploit the re-weighting schema and the Integral Probability Metric (IPM) distance to derive an upper bound on the counterfactual loss of estimating the average dose-response function (ADRF), and herein the IPM distance builds a bridge from a source (factual) domain to an infinite number of target (counterfactual) domains. We provide a discretized approximation of the IPM distance with a theoretical guarantee in the practical implementation. Based on the theoretical analyses, we also propose a novel algorithm, called Average Dose- response estiMatIon via re-weighTing schema (ADMIT). ADMIT simultaneously learns a re-weighting network, which aims to alleviate the selection bias, and an inference network, which makes factual and counterfactual estimations. In addition, the effectiveness of ADMIT is empirically demonstrated in both synthetic and semi-synthetic experiments by outperforming the existing benchmarks.

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AAAI Conference 2020 Conference Paper

Multi-Label Causal Feature Selection

Xingyu Wu
Bingbing Jiang
Kui Yu
Huanhuan Chen
Chunyan Miao

Multi-label feature selection has received considerable attentions during the past decade. However, existing algorithms do not attempt to uncover the underlying causal mechanism, and individually solve different types of variable relationships, ignoring the mutual effects between them. Furthermore, these algorithms lack of interpretability, which can only select features for all labels, but cannot explain the correlation between a selected feature and a certain label. To address these problems, in this paper, we theoretically study the causal relationships in multi-label data, and propose a novel Markov blanket based multi-label causal feature selection (MB-MCF) algorithm. MB-MCF mines the causal mechanism of labels and features ﬁrst, to obtain a complete representation of information about labels. Based on the causal relationships, MB- MCF then selects predictive features and simultaneously distinguishes common features shared by multiple labels and label-speciﬁc features owned by single labels. Experiments on real-world data sets validate that MB-MCF could automatically determine the number of selected features and simultaneously achieve the best performance compared with state-of-the-art methods. An experiment in Emotions data set further demonstrates the interpretability of MB-MCF.

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AAAI Conference 2019 Conference Paper

Joint Semi-Supervised Feature Selection and Classification through Bayesian Approach

Bingbing Jiang
Xingyu Wu
Kui Yu
Huanhuan Chen

With the increasing data dimensionality, feature selection has become a fundamental task to deal with high-dimensional data. Semi-supervised feature selection focuses on the problem of how to learn a relevant feature subset in the case of abundant unlabeled data with few labeled data. In recent years, many semi-supervised feature selection algorithms have been proposed. However, these algorithms are implemented by separating the processes of feature selection and classifier training, such that they cannot simultaneously select features and learn a classifier with the selected features. Moreover, they ignore the difference of reliability inside unlabeled samples and directly use them in the training stage, which might cause performance degradation. In this paper, we propose a joint semi-supervised feature selection and classification algorithm (JSFS) which adopts a Bayesian approach to automatically select the relevant features and simultaneously learn a classifier. Instead of using all unlabeled samples indiscriminately, JSFS associates each unlabeled sample with a self-adjusting weight to distinguish the difference between them, which can effectively eliminate the irrelevant unlabeled samples via introducing a left-truncated Gaussian prior. Experiments on various datasets demonstrate the effectiveness and superiority of JSFS.

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IS Journal 2015 Journal Article

Knowledge Engineering with Big Data

Xindong Wu
Huanhuan Chen
Gongqing Wu
Jun Liu
Qinghua Zheng
Xiaofeng He
Aoying Zhou
Zhong-Qiu Zhao

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation.

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IJCAI Conference 2015 Conference Paper

Model Metric Co-Learning for Time Series Classification

Huanhuan Chen
Fengzhen Tang
Peter Tino
Anthony G. Cohn
Xin Yao

We present a novel model-metric co-learning (MMCL) methodology for sequence classification which learns in the model space – each data item (sequence) is represented by a predictive model from a carefully designed model class. MMCL learning encourages sequences from the same class to be represented by ‘close’ model representations, well separated from those for different classes. Existing approaches to the problem either fit a single model to all the data, or a (predominantly linear) model on each sequence. We introduce a novel hybrid approach spanning the two extremes. The model class we use is a special form of adaptive high-dimensional non-linear state space model with a highly constrained and simple dynamic part. The dynamic part is identical for all data items and acts as a temporal filter providing a rich pool of dynamic features that can be selectively extracted by individual (static) linear readout mappings representing the sequences. Alongside learning the dynamic part, we also learn the global metric in the model readout space. Experiments on synthetic and benchmark data sets confirm the effectiveness of the algorithm compared to a variety of alternative methods.

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IJCAI Conference 2011 Conference Paper

Buried Utility Pipeline Mapping Based on Multiple Spatial Data Sources: A Bayesian Data Fusion Approach

Huanhuan Chen
Anthony G. Cohn

Statutory records of underground utility apparatus (such as pipes andcables) are notoriously inaccurate, so street surveys are usually undertakenbefore road excavation takes place to minimize the extent and duration ofexcavation and for health and safety reasons. This involves the use ofsensors such as Ground Penetrating Radar (GPR). The GPR scans are thenmanually interpreted and combined with the expectations from the utilityrecords and other data such as surveyed manholes. The task is complex owingto the difficulty in interpreting the sensor data, and the spatialcomplexity and extent of under street assets. We explore the application ofAI techniques, in particular Bayesian data fusion (BDF), to automaticallygenerate maps of buried apparatus. Hypotheses about the spatial location anddirection of buried assets are extracted by identifying hyperbolae in theGPR scans. The spatial location of surveyed manholes provides further inputto the algorithm, as well as the prior expectations from the statutoryrecords. These three data sources are used to produce the most probable mapof the buried assets. Experimental results on real and simulated data setsare presented.

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