I am a PhD candidate at Brown University and my area of research is Machine Learning and Computer Vision.
Drawing samples from a multivariate normal distribution subject to quadratic constraints
Do the principal components change if we apply PCA more than once (recursively) on data?
True or false? "sum of an m-strongly convex and a convex function is m-strongly convex"