Abstract Stationary Subspace Analysis (SSA)[3] is an unsupervised learning method that
finds subspaces in which data distributions stay invariant over time. It has been shown to be …

We propose a method called ideal regression for approximating an arbitrary system of
polynomial equations by a system of a particular type. Using techniques from approximate …

This Supplementary Material contains the theoretical background for a treatment of the ideal
regression problem. In Section A we explain why ideal regression is the correct framework to …

We propose a novel algebraic framework for treating probability distributions represented by
their cumulants such as the mean and covariance matrix. As an example, we consider the …

In many empirical sciences, understanding changes in distributions is an important problem.
Often one deals with signals which have been recorded in experiments over a period of …

We show that the use of techniques from algebra and algebraic geometry can be highly
beneficial for tackling machine learning problems, where the set of desired solutions can be …