

Wasserstein gradient flows and applications to sampling in machine learning - lecture 1
De Anna Korba


Wasserstein gradient flows and applications to sampling in machine learning - lecture 2
De Anna Korba
Apparaît dans la collection : 2016 - T1 - WS4 - Inference problems theme
We survey several works regarding the complexity of testing global properties of discrete distributions, when given access to only a few samples from the distribution. Such properties might include testing if two distributions have small statistical distance, testing various independence properties, testing whether a distribution has a specific shape (such as monotone decreasing, k-modal, k-histogram, monotone hazard rate,…), and approximating the entropy. We describe bounds for such testing problems whose sample complexities are sublinear in the size of the support.