There are a few mathematical results that any researcher in applied mathematics uses on a daily basis. One of them is Jensen’s inequality, which allows bounding expectations of functions of random variables. This really happens a lot in any probabilistic arguments but also as a tool to generate inequalities and optimization algorithms. In this blog post, I will present a collection of fun facts about the inequality, from very classical to more obscure. If you know other cool ones, please ad...| Machine Learning Research Blog
Symmetric positive semi-definite (PSD) matrices come up in a variety of places in machine learning, statistics, and optimization, and more generally in most domains of applied mathematics. When estimating or optimizing over the set of such matrices, several geometries can be used. The most direct one is to consider PSD matrices as a convex set in the vector space of all symmetric matrices and thus to inherit the Euclidean geometry. Like for the positive orthant (which corresponds to diagonal ...| Machine Learning Research Blog