Conditional Completion Ranking for Tabular Self-Supervised Learning

Self-supervised learning (SSL) for tabular data aims to capture dependencies among heterogeneous features. However, existing reconstruction and view-alignment objectives often emphasize target prediction or instance-level invariance, which may not fully capture the complex, multi-modal relationships among tabular features. We instead formulate tabular SSL as learning the *conditional-to-marginal shift*, which measures how the probability of a multi-column candidate completion changes after conditioning on the observed features relative to its marginal probability. Based on this formulation, we propose Conditional Completion Ranking (CCR), which distinguishes samples from the joint distribution from samples drawn from the product of marginals. We show that the optimal CCR score recovers the log conditional-to-marginal density ratio, without explicitly modeling the full conditional distribution. Experiments on 50 datasets from two benchmarks show that CCR achieves the best average rank in the few-shot settings under both frozen and fine-tuned evaluation. The code is available in the supplementary materials.