Data Science Seminar

Please note that the list below only shows forthcoming events, which may not include regular events that have not yet been entered for the forthcoming term. Please see the past events page for a list of all seminar series that the department has on offer.

Past events in this series
8 November 2021
14:00
Abstract

The success of operator splitting techniques for convex optimization has led to an explosion of methods for solving large-scale and non convex optimization problems via convex relaxation. 

This success is at the cost of overlooking direct approaches to operator splitting that embrace some of the more inconvenient aspects of many model problems, namely nonconvexity, non smoothness and infeasibility.  I will introduce some of the tools we have developed for handling these issues, and present sketches of the basic results we can obtain.

The formalism is in general metric spaces, but most applications have their basis in Euclidean spaces.  Along the way I will try to point out connections to other areas of intense interest, such as optimal mass transport.

  • Data Science Seminar
29 November 2021
14:00
Nikolas Kantas
Abstract

We consider the problem of parameter estimation for a McKean stochastic differential equation, and the associated system of weakly interacting particles. The problem is motivated by many applications in areas such as neuroscience, social sciences (opinion dynamics, cooperative behaviours), financial mathematics, statistical physics. We will first survey some model properties related to propagation of chaos and ergodicity and then move on to discuss the problem of parameter estimation both in offline and on-line settings. In the on-line case, we propose an online estimator, which evolves according to a continuous-time stochastic gradient descent algorithm on the asymptotic log-likelihood of the interacting particle system. The talk will present our convergence results and then show some numerical results for two examples, a linear mean field model and a stochastic opinion dynamics model. This is joint work with Louis Sharrock, Panos Parpas and Greg Pavliotis. Preprint: https://arxiv.org/abs/2106.13751

  • Data Science Seminar
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