Selected work

Representative Papers

Learning from a single labeled face and a stream of unlabeled data

Apr 30, 2026

This work addresses the challenging scenario of single-sample-per-person face recognition—common in personal device authentication—where only one labeled image per individual is available and no negative samples from other identities exist. The problem is formulated as a one-class classification task, and the study introduces a novel approach that leverages a continuous stream of unlabeled data to enhance model performance under this extreme data scarcity. By adopting a non-parametric modeling strategy, the method enables effective learning without requiring negative examples and provides practical guidelines for parameter selection. Evaluated on a dataset of 43 subjects, the proposed approach achieves a 90% identification rate with near-zero false positives, yielding a recall improvement of over 25% compared to the strongest baseline.

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Conditional anomaly detection with soft harmonic functions

Apr 23, 2026

This study addresses the challenge of identifying reliable anomaly labels in conditional anomaly detection, where annotated anomalies may include distributional boundaries or isolated outliers rather than true anomalies. To tackle this issue, the authors propose a novel non-parametric approach based on soft harmonic functions that explicitly models label confidence and incorporates a regularization mechanism to distinguish genuine anomalies from ambiguous cases. This work is the first to apply soft harmonic functions to conditional anomaly detection, synergistically combining non-parametric estimation with regularized optimization to significantly enhance anomaly label identification performance. Extensive experiments demonstrate that the proposed method consistently outperforms existing baselines across multiple synthetic datasets, UCI benchmarks, and real-world electronic health records, successfully uncovering anomalous patient management decisions.

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Conditional anomaly detection using soft harmonic functions: An application to clinical alerting

Apr 23, 2026

This work addresses the challenge of label misannotation in clinical practice, where anomalous events often manifest as omissions of critical procedures such as laboratory tests. The authors propose a nonparametric conditional anomaly detection method based on a soft harmonic function to estimate label confidence and identify mislabeled instances. To enhance robustness, the approach incorporates distributional support boundary regularization, which prevents isolated or marginally located samples from being erroneously classified as anomalies. Requiring no strong parametric assumptions about the underlying model, the method demonstrates significant performance gains over existing baselines on real-world electronic health record data, thereby improving the reliability and practical utility of anomaly detection in clinical settings.

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Spectral bandits for smooth graph functions

Apr 20, 2026

This work addresses the problem of online multi-armed bandits with smooth rewards over graph structures, where the expected rewards of nodes—such as recommendation items—exhibit smoothness with respect to the underlying graph. The authors introduce the key notion of “graph effective dimension” and leverage spectral graph theory together with the smoothness assumption to design two efficient algorithms. These algorithms achieve computational complexity that scales linearly or sublinearly with the effective dimension, thereby eliminating dependence on the total number of nodes. Empirical results on real-world content recommendation tasks demonstrate that user preferences over thousands of items can be accurately estimated using feedback from only dozens of nodes, substantially enhancing scalability and learning efficiency in large-scale graph settings.

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Recent publications

Latest Papers

Learning from a single labeled face and a stream of unlabeled data

Apr 30, 2026

This work addresses the challenging scenario of single-sample-per-person face recognition—common in personal device authentication—where only one labeled image per individual is available and no negative samples from other identities exist. The problem is formulated as a one-class classification task, and the study introduces a novel approach that leverages a continuous stream of unlabeled data to enhance model performance under this extreme data scarcity. By adopting a non-parametric modeling strategy, the method enables effective learning without requiring negative examples and provides practical guidelines for parameter selection. Evaluated on a dataset of 43 subjects, the proposed approach achieves a 90% identification rate with near-zero false positives, yielding a recall improvement of over 25% compared to the strongest baseline.

0 citationsRead paper

Conditional anomaly detection with soft harmonic functions

Apr 23, 2026

This study addresses the challenge of identifying reliable anomaly labels in conditional anomaly detection, where annotated anomalies may include distributional boundaries or isolated outliers rather than true anomalies. To tackle this issue, the authors propose a novel non-parametric approach based on soft harmonic functions that explicitly models label confidence and incorporates a regularization mechanism to distinguish genuine anomalies from ambiguous cases. This work is the first to apply soft harmonic functions to conditional anomaly detection, synergistically combining non-parametric estimation with regularized optimization to significantly enhance anomaly label identification performance. Extensive experiments demonstrate that the proposed method consistently outperforms existing baselines across multiple synthetic datasets, UCI benchmarks, and real-world electronic health records, successfully uncovering anomalous patient management decisions.

0 citationsRead paper

Conditional anomaly detection using soft harmonic functions: An application to clinical alerting

Apr 23, 2026

This work addresses the challenge of label misannotation in clinical practice, where anomalous events often manifest as omissions of critical procedures such as laboratory tests. The authors propose a nonparametric conditional anomaly detection method based on a soft harmonic function to estimate label confidence and identify mislabeled instances. To enhance robustness, the approach incorporates distributional support boundary regularization, which prevents isolated or marginally located samples from being erroneously classified as anomalies. Requiring no strong parametric assumptions about the underlying model, the method demonstrates significant performance gains over existing baselines on real-world electronic health record data, thereby improving the reliability and practical utility of anomaly detection in clinical settings.

0 citationsRead paper

Spectral bandits for smooth graph functions

Apr 20, 2026

This work addresses the problem of online multi-armed bandits with smooth rewards over graph structures, where the expected rewards of nodes—such as recommendation items—exhibit smoothness with respect to the underlying graph. The authors introduce the key notion of “graph effective dimension” and leverage spectral graph theory together with the smoothness assumption to design two efficient algorithms. These algorithms achieve computational complexity that scales linearly or sublinearly with the effective dimension, thereby eliminating dependence on the total number of nodes. Empirical results on real-world content recommendation tasks demonstrate that user preferences over thousands of items can be accurately estimated using feedback from only dozens of nodes, substantially enhancing scalability and learning efficiency in large-scale graph settings.

0 citationsRead paper