Talks

Data-Efficient Machine Learning in Robotics
Data efficiency, i.e., learning from small datasets, is of practical importance in many real-world applications and decision-making …
Mar 30, 2023
Data-Efficient Machine Learning in Robotics
Data efficiency, i.e., learning from small datasets, is of practical importance in many real-world applications and decision-making …
Nov 6, 2022
Tutorial on Bayesian Optimization
Bayesian optimization is a useful tool for fast optimization of black-box functions. Typically, Bayesian optimization relies on …
Sep 7, 2022
The Role of Uncertainty in Model-based Reinforcement Learning
Sep 6, 2022
Large-Scale Spatio-Temporal Inference via Message Passing
Estimating the latent state of a dynamical system based on noisy observations is a common challenge underlying many tasks in …
Jul 7, 2022
Iterative State Estimation With Approximate EP
Estimating the latent state of a dynamical system based on noisy observations is a common challenge underlying many tasks in …
May 11, 2022
Meta Learning via Bayesian Inference
May 10, 2022
Slides
Bayesian Inference for Data-Efficient Reinforcement Learning
In many high-impact areas of machine learning, we face the challenge of data-efficient learning, i.e., learning from scarce data. This …
Feb 17, 2022
Bayesian Optimization in Theory and Practice
Bayesian optimization is a useful tool for sample-efficient optimization of expensive-to-evaluate black-box functions. In the first …
Oct 15, 2021
Practical and Data-efficient Learning in Robotics
On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, …
Oct 8, 2021