Professor Marc Deisenroth is the DeepMind Chair of Machine Learning and Artificial Intelligence at University College London and the Deputy Director of the UCL Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg. Marc leads the Statistical Machine Learning Group at UCL. His research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making with applications in climate/weather science and robotics.

Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO Chair at ICML 2020, Tutorials Chair at NeurIPS 2021, and Program Chair at ICLR 2022. He is an elected member of the ICML Board. He received Paper Awards at ICRA 2014, ICCAS 2016, ICML 2020, and AISTATS 2021. In 2019, Marc co-organized the Machine Learning Summer School in London.

In 2018, Marc received The President’s Award for Outstanding Early Career Researcher at Imperial College. He is a recipient of a Google Faculty Research Award and a Microsoft PhD Grant.

In 2018, Marc spent four months at the African Institute for Mathematical Sciences (Rwanda), where he taught a course on Foundations of Machine Learning as part of the African Masters in Machine Intelligence. He is co-author of the book Mathematics for Machine Learning, published by Cambridge University Press.

**Machine Learning:** Data-efficient machine learning, Gaussian processes, reinforcement learning, Bayesian optimization, approximate inference, deep probabilistic models

**Robotics and Control:** Robot learning, legged locomotion, planning under uncertainty, imitation learning, adaptive control, robust control, learning control, optimal control

**Signal Processing:** Nonlinear state estimation, Kalman filtering, time-series modeling, dynamical systems, system identification, stochastic information processing

Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation

Bayesian inference in non-linear dynamical systems seeks to find good posterior approximations of a latent state given a sequence of observations. Gaussian filters and smoothers, including the (extended/unscented) Kalman filter/smoother, which are commonly used in engineering applications, yield Gaussian posteriors on the latent state. While they are computationally efficient, they are often criticised for their crude approximation of the posterior state distribution. In this paper, we address this criticism by proposing a message passing scheme for iterative state estimation in non-linear dynamical systems, which yields more informative (Gaussian) posteriors on the latent states. Our message passing scheme is based on expectation propagation (EP). We prove that classical Rauch–Tung–Striebel (RTS) smoothers, such as the extended Kalman smoother (EKS) or the unscented Kalman smoother (UKS), are special cases of our message passing scheme. Running the message passing scheme more than once can lead to significant improvements of the classical RTS smoothers, so that more informative state estimates can be obtained. We address potential convergence issues of EP by generalising our state estimation framework to damped updates and the consideration of general alpha-divergences.

Optimal Transport for Offline Imitation Learning.
*Proceedings of the International Conference on Learning Representations (ICLR)*.

(2023).
Actually Sparse Variational Gaussian Processes.
*Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)*.

(2023).
Optimal Transport for Offline Imitation Learning.
*NeurIPS Workshop on Offline Reinforcement Learning*.

(2022).
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes.
*NeurIPS Workshop on Tackling Climate Change with Machine Learning*.

(2022).
One-Shot Transfer of Affordance Regions? AffCorrs!.
*Proceedings of the Conference on Robot Learning (CoRL)*.

(2022).