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Byron Choi

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4 papers

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AAAI Conference 2026 Conference Paper

Gated Variational Graph Autoencoders as Experts with Competition and Consensus for Multi-view Clustering

Zhaoliang Chen
William K. Cheung
Hong-Ning Dai
Byron Choi
Jiming Liu

Multi-view clustering has been found useful to leverage diverse data sources for accurate and robust underlying data representations. It typically relies on effectively integrating the latent features from different views through allocating weights while simultaneously mining their specificity and consensus information. However, it remains open how to achieve a more fine-grained sample-level weight allocation for promoting view-specific information fusion and view-shared consensus. To address this problem, we propose a novel multi-expert learning framework named Gated Variational Graph AutoEncoder with Competition and Consensus (GVGAE-C2). In particular, it employs multiple view-specific Variational Graph AutoEncoders (VGAEs) as experts to capture the latent features from their own views. Furthermore, we design a fine-grained structure-aware gating network, which dynamically computes sample-level weights based on the proposed structure-aware quality evaluation on each expert, thus facilitating competition among experts. Meanwhile, each expert is trained not only to study its assigned view's specificity features, but also explicitly encouraged to learn consensus-aware features across views. Extensive multi-view clustering experiments on benchmark datasets reveal that GVGAE-C2 significantly outperforms state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

SVP-T: A Shape-Level Variable-Position Transformer for Multivariate Time Series Classification

Rundong Zuo
Guozhong Li
Byron Choi
Sourav S Bhowmick
Daphne Ngar-yin Mah
Grace L.H. Wong

Multivariate time series classiﬁcation (MTSC), one of the most fundamental time series applications, has not only gained substantial research attentions but has also emerged in many real-life applications. Recently, using transformers to solve MTSC has been reported. However, current transformer-based methods take data points of individual timestamps as inputs (timestamp-level), which only capture the temporal dependencies, not the dependencies among variables. In this paper, we propose a novel method, called SVP-T. Specifically, we ﬁrst propose to take time series subsequences, which can be from different variables and positions (time interval), as the inputs (shape-level). The temporal and variable dependencies are both handled by capturing the long- and short-term dependencies among shapes. Second, we propose a variable-position encoding layer (VP-layer) to utilize both the variable and position information of each shape. Third, we introduce a novel VP-based (Variable-Position) self-attention mechanism to allow the enhancing the attention weights of overlapping shapes. We evaluate our method on all UEA MTS datasets. SVP-T achieves the best accuracy rank when compared with several competitive state-of-the-art methods. Furthermore, we demonstrate the effectiveness of the VP-layer and the VP-based self-attention mechanism. Finally, we present one case study to interpret the result of SVP-T.

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IJCAI Conference 2021 Conference Paper

Graph Edit Distance Learning via Modeling Optimum Matchings with Constraints

Yun Peng
Byron Choi
Jianliang Xu

Graph edit distance (GED) is a fundamental measure for graph similarity analysis in many real applications. GED computation has known to be NP-hard and many heuristic methods are proposed. GED has two inherent characteristics: multiple optimum node matchings and one-to-one node matching constraints. However, these two characteristics have not been well considered in the existing learning-based methods, which leads to suboptimal models. In this paper, we propose a novel GED-specific loss function that simultaneously encodes the two characteristics. First, we propose an optimal partial node matching-based regularizer to encode multiple optimum node matchings. Second, we propose a plane intersection-based regularizer to impose the one-to-one constraints for the encoded node matchings. We use the graph neural network on the association graph of the two input graphs to learn the cross-graph representation. Our experiments show that our method is 4. 2x-103. 8x more accurate than the state-of-the-art methods on real-world benchmark graphs.

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AAAI Conference 2021 Conference Paper

ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification

Guozhong Li
Byron Choi
Jianliang Xu
Sourav S Bhowmick
Kwok-Pan Chun
Grace Lai-Hung Wong

Time series shapelets are short discriminative subsequences that recently have been found not only to be accurate but also interpretable for the classification problem of univariate time series (UTS). However, existing work on shapelets selection cannot be applied to multivariate time series classification (MTSC) since the candidate shapelets of MTSC may come from different variables of different lengths and thus cannot be directly compared. To address this challenge, in this paper, we propose a novel model called ShapeNet, which embeds shapelet candidates of different lengths into a unified space for shapelet selection. The network is trained using cluster-wise triplet loss, which considers the distance between anchor and multiple positive (negative) samples and the distance between positive (negative) samples, which are important for convergence. We compute representative and diversified final shapelets rather than directly using all the embeddings for model building to avoid a large fraction of non-discriminative shapelet candidates. We have conducted experiments on ShapeNet with competitive state-of-the-art and benchmark methods using UEA MTS datasets. The results show that the accuracy of ShapeNet is the best of all the methods compared. Furthermore, we illustrate the shapelets’ interpretability with two case studies.

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