DAG Learning from Zero-Inflated Count Data Using Continuous Optimization
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917153628749824 |
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| author | Sato, Noriaki Scutari, Marco Kawano, Shuichi Yamaguchi, Rui Imoto, Seiya |
| author_facet | Sato, Noriaki Scutari, Marco Kawano, Shuichi Yamaguchi, Rui Imoto, Seiya |
| contents | We address network structure learning from zero-inflated count data by casting each node as a zero-inflated generalized linear model and optimizing a smooth, score-based objective under a directed acyclic graph constraint. Our Zero-Inflated Continuous Optimization (ZICO) approach uses node-wise likelihoods with canonical links and enforces acyclicity through a differentiable surrogate constraint combined with sparsity regularization. ZICO achieves superior performance with faster runtimes on simulated data. It also performs comparably to or better than common algorithms for reverse engineering gene regulatory networks. ZICO is fully vectorized and mini-batched, enabling learning on larger variable sets with practical runtimes in a wide range of domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DAG Learning from Zero-Inflated Count Data Using Continuous Optimization Sato, Noriaki Scutari, Marco Kawano, Shuichi Yamaguchi, Rui Imoto, Seiya Machine Learning We address network structure learning from zero-inflated count data by casting each node as a zero-inflated generalized linear model and optimizing a smooth, score-based objective under a directed acyclic graph constraint. Our Zero-Inflated Continuous Optimization (ZICO) approach uses node-wise likelihoods with canonical links and enforces acyclicity through a differentiable surrogate constraint combined with sparsity regularization. ZICO achieves superior performance with faster runtimes on simulated data. It also performs comparably to or better than common algorithms for reverse engineering gene regulatory networks. ZICO is fully vectorized and mini-batched, enabling learning on larger variable sets with practical runtimes in a wide range of domains. |
| title | DAG Learning from Zero-Inflated Count Data Using Continuous Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.16233 |