Selected work

Representative Papers

Interpretable Fuzzy Inference for UAV Target Tracking Using Bounding-Box Geometry

Aug 04, 2026

This study addresses the challenges of perceptual uncertainty, high computational cost, and limited interpretability in continuous yaw angle estimation for ground targets by resource-constrained drones under visual guidance. To this end, the authors propose an interpretable fuzzy inference framework leveraging geometric features—center position, area, and aspect ratio—extracted from YOLO detection bounding boxes. The approach innovatively integrates Mamdani and Takagi–Sugeno fuzzy systems, constructing a compact rule base of only 27 rules derived from quantiles of the training data, thereby generating continuous yaw commands without explicit geometric modeling. Evaluated on 6,169 samples, the Takagi–Sugeno variant achieves a mean absolute error of 0.140°, 99.676% accuracy within ±1°, and 90.254% directional consistency, demonstrating high precision, strong interpretability, and suitability for real-time deployment.

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Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data

Jul 31, 2026

This study addresses the performance degradation and clustering bias of unsupervised methods on class-imbalanced tabular data. It presents the first systematic evaluation of deep clustering approaches under varying degrees of class imbalance and introduces a novel ensemble mechanism that integrates clustering results from multiple embedding dimensions while combining several high-performing algorithms through majority voting. Evaluated on 16 imbalanced tabular datasets, the proposed method consistently outperforms individual clustering models, achieving notable improvements in Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI). These gains demonstrate its enhanced capability to recover the underlying true class structure in imbalanced settings.

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Confidence-Gated Vision-Only Heading Alignment for UAV-UGV Cooperative Systems

Jul 21, 2026

This work addresses the unreliability of control commands in vision-only UAV-UGV cooperative heading prediction when perception confidence is low, which often leads to blind and erroneous execution. To mitigate this issue, the authors propose a lightweight confidence-gated framework that leverages two interpretable proxy metrics—bounding box area and short-term heading variation—to assess perception reliability. During low-confidence intervals, the framework conservatively updates control commands using either a freeze strategy or a novel bounded fusion fallback mechanism. This study is the first to integrate interpretable reliability proxies with a decision-aware command issuance policy, revealing the limitations of freezing strategies under sparse execution and demonstrating that bounded fusion significantly enhances command-level stability. Experiments on real-world datasets show that the approach effectively balances execution rate, accuracy, and smoothness, with bounded fusion substantially reducing stale-command errors in low-confidence scenarios.

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Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks

Jul 11, 2026

This work addresses the computational challenges in Bayesian DAG structure learning arising from the super-exponential growth of the DAG space and the intractability of marginal likelihoods under non-conjugate priors. The authors propose a novel approach based on a modified Cholesky parameterization of the precision matrix, deriving—for the first time—the node-wise marginal likelihood under a Normal–Gamma non-conjugate prior in the form of a generalized inverse Gaussian distribution. Leveraging its asymptotic properties for large parameters, they construct an efficient Laplace approximation-based scoring function embedded within a Metropolis–Hastings sampler for DAG search. Additionally, a probit link couples latent variables with binary clinical outcomes. This framework overcomes the limitations of conjugate priors and enables exact posterior sampling of conditional variances. Experiments demonstrate superior performance over PC, GES, NOTEARS, DAGMA, and conjugate baselines on synthetic data, successful recovery of known structures in the Sachs signaling pathway and Wisconsin breast cancer datasets, and accurate prediction of malignancy with a cross-validated ROC-AUC of 0.94.

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Bayesian DAG Structure Learning with Simultaneous Shrinkage Covariance Estimation under Scale-Mixture Error Distributions in the Proportional High-Dimensional Regime

Jul 09, 2026

This study addresses the challenge of jointly estimating directed acyclic graph (DAG) structures and precision matrices in high-dimensional settings with heavy-tailed or contaminated data. The authors propose R-DACH, a unified Bayesian framework that, for the first time, directly places a global–local horseshoe prior on the strictly lower triangular part of the Cholesky factor, combined with an inverse-Gamma scale mixture error model. This approach enables simultaneous inference of variable ordering, sparse DAG structure, and continuous parameters while inherently achieving robustness to outliers. Theoretical and empirical results demonstrate that R-DACH outperforms graphical horseshoe, DAG-Wishart, and PC algorithms—particularly under contamination—with superior topological consistency and parent selection accuracy, even when the number of variables reaches several hundred. Applied to TCGA RNA-seq data, R-DACH uncovers biologically interpretable gene regulatory relationships missed by competing methods.

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Recent publications

Latest Papers

Interpretable Fuzzy Inference for UAV Target Tracking Using Bounding-Box Geometry

Aug 04, 2026

This study addresses the challenges of perceptual uncertainty, high computational cost, and limited interpretability in continuous yaw angle estimation for ground targets by resource-constrained drones under visual guidance. To this end, the authors propose an interpretable fuzzy inference framework leveraging geometric features—center position, area, and aspect ratio—extracted from YOLO detection bounding boxes. The approach innovatively integrates Mamdani and Takagi–Sugeno fuzzy systems, constructing a compact rule base of only 27 rules derived from quantiles of the training data, thereby generating continuous yaw commands without explicit geometric modeling. Evaluated on 6,169 samples, the Takagi–Sugeno variant achieves a mean absolute error of 0.140°, 99.676% accuracy within ±1°, and 90.254% directional consistency, demonstrating high precision, strong interpretability, and suitability for real-time deployment.

0 citationsRead paper

Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data

Jul 31, 2026

This study addresses the performance degradation and clustering bias of unsupervised methods on class-imbalanced tabular data. It presents the first systematic evaluation of deep clustering approaches under varying degrees of class imbalance and introduces a novel ensemble mechanism that integrates clustering results from multiple embedding dimensions while combining several high-performing algorithms through majority voting. Evaluated on 16 imbalanced tabular datasets, the proposed method consistently outperforms individual clustering models, achieving notable improvements in Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI). These gains demonstrate its enhanced capability to recover the underlying true class structure in imbalanced settings.

0 citationsRead paper

Confidence-Gated Vision-Only Heading Alignment for UAV-UGV Cooperative Systems

Jul 21, 2026

This work addresses the unreliability of control commands in vision-only UAV-UGV cooperative heading prediction when perception confidence is low, which often leads to blind and erroneous execution. To mitigate this issue, the authors propose a lightweight confidence-gated framework that leverages two interpretable proxy metrics—bounding box area and short-term heading variation—to assess perception reliability. During low-confidence intervals, the framework conservatively updates control commands using either a freeze strategy or a novel bounded fusion fallback mechanism. This study is the first to integrate interpretable reliability proxies with a decision-aware command issuance policy, revealing the limitations of freezing strategies under sparse execution and demonstrating that bounded fusion significantly enhances command-level stability. Experiments on real-world datasets show that the approach effectively balances execution rate, accuracy, and smoothness, with bounded fusion substantially reducing stale-command errors in low-confidence scenarios.

0 citationsRead paper

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks

Jul 11, 2026

This work addresses the computational challenges in Bayesian DAG structure learning arising from the super-exponential growth of the DAG space and the intractability of marginal likelihoods under non-conjugate priors. The authors propose a novel approach based on a modified Cholesky parameterization of the precision matrix, deriving—for the first time—the node-wise marginal likelihood under a Normal–Gamma non-conjugate prior in the form of a generalized inverse Gaussian distribution. Leveraging its asymptotic properties for large parameters, they construct an efficient Laplace approximation-based scoring function embedded within a Metropolis–Hastings sampler for DAG search. Additionally, a probit link couples latent variables with binary clinical outcomes. This framework overcomes the limitations of conjugate priors and enables exact posterior sampling of conditional variances. Experiments demonstrate superior performance over PC, GES, NOTEARS, DAGMA, and conjugate baselines on synthetic data, successful recovery of known structures in the Sachs signaling pathway and Wisconsin breast cancer datasets, and accurate prediction of malignancy with a cross-validated ROC-AUC of 0.94.

0 citationsRead paper

Bayesian DAG Structure Learning with Simultaneous Shrinkage Covariance Estimation under Scale-Mixture Error Distributions in the Proportional High-Dimensional Regime

Jul 09, 2026

This study addresses the challenge of jointly estimating directed acyclic graph (DAG) structures and precision matrices in high-dimensional settings with heavy-tailed or contaminated data. The authors propose R-DACH, a unified Bayesian framework that, for the first time, directly places a global–local horseshoe prior on the strictly lower triangular part of the Cholesky factor, combined with an inverse-Gamma scale mixture error model. This approach enables simultaneous inference of variable ordering, sparse DAG structure, and continuous parameters while inherently achieving robustness to outliers. Theoretical and empirical results demonstrate that R-DACH outperforms graphical horseshoe, DAG-Wishart, and PC algorithms—particularly under contamination—with superior topological consistency and parent selection accuracy, even when the number of variables reaches several hundred. Applied to TCGA RNA-seq data, R-DACH uncovers biologically interpretable gene regulatory relationships missed by competing methods.

0 citationsRead paper