[R] Joint Embedding Variational Bayes (TMLR ’26)

This TMLR paper introduces operational variational semantics to joint-embedding architectures for non-contrastive representation learning. It does so by factorizing embedding likelihood, anchoring posterior uncertainty to likelihood scale, and employing a heavy-tailed Student-t likelihood for empirical benefits.