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 …
On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, reinforcement learning (RL) is a promising framework for learning to solve problems by trial and error. While RL has had …
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 robot learning we face challenge of data-efficient learning. In this talk, we will make the case for three types of useful models that become handy in robot learning: probabilistic models, hierarchical models, and models that allow us to …