用连续优化方法学习零膨胀计数数据的因果网络结构。
DAG Learning from Zero-Inflated Count Data Using Continuous Optimization
- 将每个节点建模为零膨胀广义线性模型，通过可微代理约束保证无环性。
- 在模拟数据上比现有方法更快且性能更优，基因调控网络反向工程表现良好。
- 支持向量化和小批量计算，适合大规模变量场景，适用范围广。
针对零膨胀计数数据的网络结构学习问题，本文将每个节点建模为零膨胀广义线性模型，并在有向无环图（DAG）约束下优化一个平滑的评分目标函数。提出的ZICO方法采用节点级似然与标准链接函数，通过可微代理约束结合稀疏正则化来强制无环性。在模拟数据上，ZICO展现出更优性能与更快运行速度；在基因调控网络反向工程任务中，其表现可媲美或优于常见算法。该方法完全向量化并支持小批量处理，可在多种领域实现大规模变量集上的高效学习，具有实际应用价值。
原文摘要 · Abstract (English)
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.
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