A zero-inflated Poisson tensor model integrating low-rank CP structure, latent cluster embeddings, and smoothness is introduced for sparse single-cell Hi-C count tensors, with a Bayes-optimal zero distinction procedure, identifiability results, and consistency rates.
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Transfer learning from informative source networks improves target DCMM estimation accuracy by enlarging the eigenvalue gap of the connection probability matrix, with algorithms to avoid negative transfer.
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Zero-inflated modeling with smoothing on counting tensors
A zero-inflated Poisson tensor model integrating low-rank CP structure, latent cluster embeddings, and smoothness is introduced for sparse single-cell Hi-C count tensors, with a Bayes-optimal zero distinction procedure, identifiability results, and consistency rates.
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Transfer Learning for Degree-Corrected Mixed Membership Network Models
Transfer learning from informative source networks improves target DCMM estimation accuracy by enlarging the eigenvalue gap of the connection probability matrix, with algorithms to avoid negative transfer.