Author

Nguyen Thai Anh

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Preprint Jul 2026

HeAD-CP: Heterophily-Aware Diffused Conformal Prediction Sets for Graph Neural Networks

HeAD-CP is proposed, a family of node-wise diffusion variants whose coefficients are determined by a label-free local-homophily estimate derived from the GNN softmax, which are most effective at extreme heterophily, intermediate heterophily, and moderate-to-high homophily, respectively, and all preserve the marginal coverage guarantee.

P. Lam, Nguyen Thai Anh · 0 citations

Preprint Jul 2026

When Does Deep Representation Learning Help Single-Cell Clustering? A Sensitivity-Aware Diagnostic Benchmark for Biomedical AI Pipelines

Per-dataset analysis reveals three reproducible regimes: probabilistic variational autoencoder variants help on the smallest datasets, deep autoencoders win on mid-scale data with multi-batch or many-type structure, and classical PCA pipelines remain competitive when linear projection already captures the dominant variation.

Phong T. Nguyen, T. Vu, Thu Ha Nguyen et al. · 0 citations