Neural Network Models for Contextual Regression
This work addresses efficiency and interpretability in regression models for domains where contextual features influence predictions, though it appears incremental as it builds on existing neural network methods.
The authors tackled the problem of contextual regression by proposing a simple contextual neural network (SCtxtNN) that separates context identification from context-specific regression, achieving lower excess mean squared error and more stable performance than feed-forward neural networks with comparable parameters.
We propose a neural network model for contextual regression in which the regression model depends on contextual features that determine the active submodel and an algorithm to fit the model. The proposed simple contextual neural network (SCtxtNN) separates context identification from context-specific regression, resulting in a structured and interpretable architecture with fewer parameters than a fully connected feed-forward network. We show mathematically that the proposed architecture is sufficient to represent contextual linear regression models using only standard neural network components. Numerical experiments are provided to support the theoretical result, showing that the proposed model achieves lower excess mean squared error and more stable performance than feed-forward neural networks with comparable numbers of parameters, while larger networks improve accuracy only at the cost of increased complexity. The results suggest that incorporating contextual structure can improve model efficiency while preserving interpretability.