Publications

You can also find my articles on my Google Scholar profile.

Journal paper

DDIR: Domain-Disentangled Invariant Representation Learning for Tailored Predictions

Published in Knowledge-Based Systems, 2025

Traditional training struggles with large datasets due to distributional differences. Domain generalization (DG) methods, like DIR learning, perform well with domain shifts but often hurt in-distribution performance. We propose DDIR learning, which preserves domain-orthogonal invariant (DOI) information without redundancy. DDIR...

Recommended citation: Y. Ma et al., "DDIR: Domain-Disentangled Invariant Representation Learning for Tailored Predictions," in Knowledge-Based Systems 2025.
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Grade-Skewed Domain Adaptation via Asymmetric Bi-Classifier Discrepancy Minimization for Diabetic Retinopathy Grading

Published in IEEE Transactions on Medical Imaging (TMI), 2025

Diabetic retinopathy (DR) is a leading cause of preventable blindness, with deep learning showing promise in its grading. However, domain shifts, small lesions, and imbalanced grade distributions complicate generalization and adaptation, often leading to biased predictions. To address this, we...

Recommended citation: Y. Ma et al., "Grade-Skewed Domain Adaptation via Asymmetric Bi-Classifier Discrepancy Minimization for Diabetic Retinopathy Grading," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2024.3485064.
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Conference paper

Class-imbalanced Time Series Adaptation via Multi-Expert Consistency Entropy Minimization

Published in IEEE International Conference on Bioinformatics and Biomedicine, 2024

Wearable-based time series recognition aims to infer behavioral classes based on time series signals collected by sensors. Domain Adaptation (DA) methods have significantly improved the performance on out-of-domain test classification tasks. However, highly imbalanced label distribution in real-world tasks hinders...

Recommended citation: Y. Ma et al., "Class-imbalanced Time Series Adaptation via Multi-Expert Consistency Entropy Minimization," 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal, 2024, pp. 2284-2290, doi: 10.1109/BIBM62325.2024.10822753.
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Letting Go of Self-Domain Awareness: Multi-Source Domain-Adversarial Generalization via Dynamic Domain-Weighted Contrastive Transfer Learning

Published in ECAI, 2023

Domain generalization (DG) aims to train models that generalize to unseen target domains, often by learning domain-invariant representations. However, overly compressed representations can confuse classes within the same domain. To address this, we propose MsCtrl, a framework incorporating dynamic domain-weighted...

Recommended citation: Y. Ma et al., " Letting Go of Self-Domain Awareness: Multi-Source Domain-Adversarial Generalization via Dynamic Domain-Weighted Contrastive Transfer Learning," in ECAI 2023. IOS Press, 2023: 1664-1671.
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Yuan Ma

Publications

Journal paper

DDIR: Domain-Disentangled Invariant Representation Learning for Tailored Predictions

Grade-Skewed Domain Adaptation via Asymmetric Bi-Classifier Discrepancy Minimization for Diabetic Retinopathy Grading

Conference paper

Class-imbalanced Time Series Adaptation via Multi-Expert Consistency Entropy Minimization

Letting Go of Self-Domain Awareness: Multi-Source Domain-Adversarial Generalization via Dynamic Domain-Weighted Contrastive Transfer Learning