[R] Joint Embedding Variational Bayes (TMLR ’26) Disclosure: first author. The paper was just published in TMLR, and I figured it might be of interest to some people here. It is fairly dense mathematically, but straightforward conceptually: to add operational variational semantics to joint-embedding architectures for non-contrastive representation learning, we make three coupled choices: * **Factorize embedding likelihood:** the likelihood is split into directional and radial terms, so angular alignment and representation norm are modelled separately. The radial/norm term does not drive accuracy on its own, but the factorization avoids the norm-direction coupling that otherwise produces pathological solutions. * **Anchor posterior/likelihood uncertainty:** the posterior variance is tied to the likelihood scale, so uncertainty directly governs both inference and the embedding likelihood. * **Use heavy-tailed likelihood:** the likelihood uses a Student-t form rather than Gaussian.…