Partial-label learning is a popular weakly supervised setting that allows each training example to be annotated with set of candidate labels. Previous studies on partial-label only focused the classification where labels are all discrete, which cannot handle continuous real values. In this paper, we provide first attempt investigate regression, real-valued To solve problem, propose simple basel...

Quantifying uncertainty in predictions or, more generally, estimating the posterior conditional distribution, is a core challenge machine learning and statistics. We introduce Convex Nonparanormal Regression (CNR), nonparanormal approach for coping with this task. CNR involves convex optimization of defined via rich dictionary pre-defined non linear transformations on Gaussians. It can fit an a...

The common practice of collapsing inherently continuous or ordinal variables into two categories causes information loss that may potentially weaken power to detect effects of explanatory variables and result in Type II errors in statistical inference. The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal instead...