Graph-based Tabular Deep Learning Should Learn Feature Interactions, Not Just Make Predictions

Despite recent progress, deep learning methods for tabular data still struggle to compete with traditional tree-based models. A key challenge lies in modeling complex, dataset-specific feature interactions that are central to tabular data. Graph-based tabular deep learning (GTDL) methods aim to address this by representing features and their interactions as graphs. However, existing methods predominantly optimize predictive accuracy, neglecting accurate modeling of the graph structure. This position paper argues that GTDL should move beyond prediction-centric objectives and prioritize the explicit learning and evaluation of feature interactions. Using synthetic datasets with known ground-truth graph structures, we show that existing GTDL methods fail to recover meaningful feature interactions. Moreover, enforcing the true interaction structure improves predictive performance. This highlights the need for GTDL methods to prioritize quantitative evaluation and accurate structural learning. We call for a shift toward structure-aware modeling as a foundation for building GTDL systems that are not only accurate but also interpretable, trustworthy, and grounded in domain understanding.

High-Dimensional Bayesian Structure Learning in Gaussian Graphical Models using Marginal Pseudo-Likelihood

Gaussian graphical models depict the conditional dependencies between variables within a multivariate normal distribution in a graphical format. The identification of these graph structures is an area known as structure learning. However, when utilizing Bayesian methodologies in structure learning, computational complexities can arise, especially with high-dimensional graphs surpassing 250 nodes. This paper introduces two innovative search algorithms that employ marginal pseudo-likelihood to address this computational challenge. These methods can swiftly generate reliable estimations for problems encompassing 1000 variables in just a few minutes on standard computers. For those interested in practical applications, the code supporting this new approach is made available through the R package BDgraph.