A novel robust matrix completion method that overcomes limitations of existing schemes, accompanied by recovery guarantees

We solve the learning dynamics of (a close approximation of) word2vec in closed form, revealing what semantic features are learned.

We introduce a modular matrix factorization framework called "covariate-moderated empirical Bayes matrix factorization" (cEBMF) that can leverage side information to improve the factorization through the use of adaptive priors.