Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric hypothesis testing and for learning on distributional inputs. I will give an overview of this framework and present some of its recent applications within the context of approximate Bayesian inference. Further, I will discuss a recent modification of MMD which aims to encode invariance to additive symmetric noise and leads to learning on distributions robust to the distributional covariate shift, e.g. where measurement noise on the training data differs from that on the testing data.
Biography: Dino is an Associate Professor at Oxford, as well as a Faculty Fellow at the Alan Turing Institute. He works at the intersection of statistics and machine learning, and is an expert in advanced kernel methods. I hope that many of you, particularly from Bernhard’s department and Ule’s group, will be interested in his talk. If you’d like a meeting with Dino, let me know. I’ll be keeping a list.