Past talks can be found below:

**Learning High-Dimensional Functions for Rare Events Sampling In and Out of Equilibrium**

**Abstract:**

The surprising flexibility and undeniable empirical success of machine learning algorithms have inspired many theoretical explanations for the efficacy of neural networks. Here, I will briefly introduce one perspective that provides not only asymptotic guarantees of trainability and accuracy in high-dimensional learning problems but also provides some prescriptions and design principles for learning. Bolstered by the favorable scaling of these algorithms in high dimensional problems, I will turn to the problem of variational rare events calculations that arise in chemical physics. With neural networks in the toolkit, I will show it is possible to generate high-dimensional representations of free energies and a nonequilibrium analog of the free energy for dynamical observables. I will describe a class of algorithms that combines stochastic gradient descent with importance sampling to accurately determine these solutions.