Institution profile

University of East Anglia

Academic institutioneurope · gb
Official website
Research library10linked papers
Opportunities0open roles
Selected work

Representative Papers

CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework

Aug 16, 2026

This study addresses the challenge of costly annotation for UAV-based ice crevasse segmentation by proposing a label-efficient framework. Integrating DINOv3 self-supervised features with a nonlinear classifier, the method reveals a performance inversion phenomenon between linear and nonlinear readouts of pretrained features, validating the benefits of satellite pretraining for remote sensing self-supervised learning. With only 24 annotated samples, the model achieves 75.33 mDSC and 61.28 mIoU, significantly outperforming conventional baselines. This work establishes a novel paradigm for high-precision, few-shot recognition in polar remote sensing, effectively reducing reliance on expert annotation.

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Tracking the Best Strategy in an Extensive-Form Game

Aug 10, 2026

This work addresses the challenge of minimizing regret against arbitrarily switching mixed strategies of forgetful opponents in extensive-form games under bandit feedback. To this end, we propose the first online learning algorithm that simultaneously achieves low switching regret and high computational efficiency. By leveraging the game tree structure and incorporating an adaptive parameter mechanism, our method effectively controls the frequency of strategy switches while optimizing the regret bound. The algorithm attains a switching regret bound of Õ((1/ρ + ρK)√(HAT)) and operates with a per-round time complexity of only O(HB), significantly enhancing both scalability and practical applicability.

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Continuous-Time Bayesian Networks with Structured Shrinkage Priors for Modelling Multimorbidity Trajectories in Large-Scale Electronic Health Records

Jul 01, 2026

Existing approaches struggle to effectively model the complex interactions among disease progression, multimorbidity networks, and high-dimensional time-varying risk factors. This work proposes a structured Bayesian continuous-time Bayesian network to learn directed disease dependencies from longitudinal electronic health records, wherein transition intensities depend on existing conditions, pairwise interactions, and exogenous covariates. To mitigate parameter explosion in higher-order terms while preserving interpretability of main effects, the model incorporates order-dependent structured shrinkage priors—such as spike-and-slab and Bayesian LASSO—that selectively suppress redundant interactions. Simulation studies demonstrate that the spike-and-slab prior achieves superior performance in variable selection, network recovery, and false discovery control. Applied to UK Biobank data, the method successfully identifies a diabetes-centered metabolic module and a respiratory–atopic inflammatory module.

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Recent publications

Latest Papers

CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework

Aug 16, 2026

This study addresses the challenge of costly annotation for UAV-based ice crevasse segmentation by proposing a label-efficient framework. Integrating DINOv3 self-supervised features with a nonlinear classifier, the method reveals a performance inversion phenomenon between linear and nonlinear readouts of pretrained features, validating the benefits of satellite pretraining for remote sensing self-supervised learning. With only 24 annotated samples, the model achieves 75.33 mDSC and 61.28 mIoU, significantly outperforming conventional baselines. This work establishes a novel paradigm for high-precision, few-shot recognition in polar remote sensing, effectively reducing reliance on expert annotation.

0 citationsRead paper

Tracking the Best Strategy in an Extensive-Form Game

Aug 10, 2026

This work addresses the challenge of minimizing regret against arbitrarily switching mixed strategies of forgetful opponents in extensive-form games under bandit feedback. To this end, we propose the first online learning algorithm that simultaneously achieves low switching regret and high computational efficiency. By leveraging the game tree structure and incorporating an adaptive parameter mechanism, our method effectively controls the frequency of strategy switches while optimizing the regret bound. The algorithm attains a switching regret bound of Õ((1/ρ + ρK)√(HAT)) and operates with a per-round time complexity of only O(HB), significantly enhancing both scalability and practical applicability.

0 citationsRead paper

Continuous-Time Bayesian Networks with Structured Shrinkage Priors for Modelling Multimorbidity Trajectories in Large-Scale Electronic Health Records

Jul 01, 2026

Existing approaches struggle to effectively model the complex interactions among disease progression, multimorbidity networks, and high-dimensional time-varying risk factors. This work proposes a structured Bayesian continuous-time Bayesian network to learn directed disease dependencies from longitudinal electronic health records, wherein transition intensities depend on existing conditions, pairwise interactions, and exogenous covariates. To mitigate parameter explosion in higher-order terms while preserving interpretability of main effects, the model incorporates order-dependent structured shrinkage priors—such as spike-and-slab and Bayesian LASSO—that selectively suppress redundant interactions. Simulation studies demonstrate that the spike-and-slab prior achieves superior performance in variable selection, network recovery, and false discovery control. Applied to UK Biobank data, the method successfully identifies a diabetes-centered metabolic module and a respiratory–atopic inflammatory module.

0 citationsRead paper