Estimating the latent state of a dynamical system based on noisy observations is common challenge underlying many task in engineering, robotics, or climate science. Classical approaches to state estimation include Kalman filtering/smoothing, which is …

In many high-impact areas of machine learning, we face the challenge of data-efficient learning, i.e., learning from scarce data. This includes healthcare, climate science, and autonomous robots. There are many approaches toward learning from scarce …

Bayesian optimization is a useful tool for sample-efficient optimization of expensive-to-evaluate black-box functions. In the first part of the talk, we will have a look at a motivating robotics example, where Bayesian optimization can be used for …

On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, machine learning is a promising framework for automatically learning to solve problems. While machine learning has had …

In many high-impact areas of machine learning, we face the challenge of data-efficient learning, i.e., learning from scarce data. This includes healthcare, climate science, and autonomous robots. There are many approaches toward learning from scarce …

Integration and differentiation play key roles in machine learning. We take a tour of some old and new results on methods and algorithms for integration and differentiation, in particular, for calculating expectations and slopes. We review numerical …

In many high-impact areas of machine learning, we face the challenge of data-efficient learning, i.e., learning from scarce data. This includes healthcare, climate science, and autonomous robots. There are many approaches toward learning from scarce …