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 …

Bayesian optimization is a useful tool for fast optimization of black-box functions. Typically, Bayesian optimization relies on Gaussian processes as a surrogate model for the unknown function, which can then be used to find a good trade-off between …

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

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

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 …