Georgia Institute of Technology College of Sciences

I will give essentially matching upper and lower bounds for the expected max-sliced 1-Wasserstein distance between a probability measure on a separable Hilbert space and its empirical distribution from n samples. A version of this result for Banach spaces will also be presented. From this, we will derive an upper bound for the expected max-sliced 2-Wasserstein distance between a symmetric probability measure on a Euclidean space and its symmetrized empirical distribution.

There are lots of ways to measure the complexity of a knot. Some come from knot diagrams, and others come from topological or geometric quantities extracted from some auxiliary space. In this talk, I’ll describe a geometry property, which we call “twist positivity”, that often puts strong restrictions on how the braid and bridge index are related. I’ll describe some old and new results about twist positivity, as well as some new applications towards knot concordance. In particular, I’ll describe how using a suite of numerical knot invariants (including the braid index) in tandem allows one to prove that there are infinitely many positive braid knots which all represent distinct smooth concordance classes. This confirms a prediction of the slice-ribbon conjecture. Everything I’ll discuss is joint work with Hugh Morton. I will assume very little background about knot invariants for this talk – all are welcome!

In this talk, we introduce and survey continuous-time deep learning approaches based on neural ordinary differential equations (neural ODEs) arising in supervised learning, generative modeling, and numerical solution of high-dimensional optimal control problems. We will highlight theoretical advantages and numerical benefits of neural ODEs in deep learning and their use to solve otherwise intractable PDE problems.

We present new results concerning characterizations of the spaces $C^{1,\alpha}$ and “$LI_{\alpha+1}$” for $0<\alpha<1$.  The space $LI_{\alpha +1}$ is the space of Lipschitz functions with $\alpha$-order fractional derivative having bounded mean oscillation.  These characterizations involve geometric square functions which measure how well the graph of a function is approximated by a hyperplane at every point and scale.  We will also discuss applications of these results to higher-order rectifiability.