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 …

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 …

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 …