Our initial investigation identifies which distributions accurately characterize the feature space, subsequently leveraging this priori to guide the alignment of the weakly supervised embeddings. Specifically, we analyze the superiority of the mixture of von Mises-Fisher distributions (moVMF) among several common distribution candidates.

Bayes' rule naturally allows for inference refinement in a streaming fashion, without the need to recompute posteriors from scratch whenever new data arrives.

In this direction, we provide a general bound under a new model-dependent assumption from which we obtain bounds based on parameter norms and log-Sobolev inequalities.

Some existing works have proposed score matching estimators for point processes.

Current uncertainty quantification is memory and compute expensive, which hinders practical uptake.

Our approach can be used with any decision-theoretic acquisition function and is readily compatible with trust region methods like TuRBO.

The premise of semi-supervised learning (SSL) is that combining labeled and unlabeled data yields significantly more accurate models.

Most recent approaches are motivated heuristically and lack a unifying framework, obscuring connections between them. Further, they often suffer from issues such as being very sensitive to hyperparameters, being expensive to train or needing access to weights hidden behind a closed API.