DAG Learning from Zero-Inflated Count Data Using Continuous Optimization
By: Noriaki Sato , Marco Scutari , Shuichi Kawano and more
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.
Similar Papers
Non-negative DAG Learning from Time-Series Data
Signal Processing
Finds hidden causes in changing data.
Flexible model for varying levels of zeros and outliers in count data
Methodology
Better counts for tricky data with many zeros.
On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
Machine Learning (CS)
Finds best answers without needing all the steps.