Cross-Block Conditioning in Deep Boltzmann Machines for Statistical Data Fusion

The paper introduces cross-block conditioning as a way to train deep Boltzmann machines on statistically fused data, where two panels share covariates but record different outcome blocks. Traditional data fusion setups never observe both outcomes for the same row, which prevents using a standard discriminative objective. The authors propose an approach that works around this missing-row problem, and the work appears as a cross-listing in arXiv's AI and machine learning categories.