Abstract:

Data efficiency, i.e., learning from small datasets, is of practical importance in many real-world applications and decision-making systems. Data efficiency can be achieved in multiple ways, such as probabilistic modeling, where models and predictions are equipped with meaningful uncertainty estimates, transfer learning, or the incorporation of valuable prior knowledge.

In this talk, I will focus on how robot learning can benefit from data-efficient learning algorithms. We will discuss three different ways to use data efficiently in reinforcement learning and robotics settings: model-based reinforcement learning, transfer learning, and offline reinforcement learning.

Bio:

Professor Marc Deisenroth is the DeepMind Chair of Machine Learning and Artificial Intelligence at University College London, Deputy Director of the UCL Centre for Artificial Intelligence, and part of the UNESCO Chair on Artificial Intelligence at UCL. He also holds a visiting faculty position at the University of Johannesburg. Marc co-leads the Sustainability and Machine Learning Group at UCL. His research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making with applications in weather, nuclear fusion, 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, AISTATS 2021, and FAccT 2023. Marc is co-author of the book Mathematics for Machine Learning, published by Cambridge University Press.