8 citations · 11 across the 6 of their papers we have counts for
3 papers · 1 filter
High-dimensional Bayesian Optimization with Group Testing
Erik Orm Hellsten, Carl Hvarfner, Leonard Papenmeier +1
Bayesian optimization is an effective method for optimizing expensive-to-evaluate black-box functions. High-dimensional problems are particularly challenging as the surrogate model…
BeBOP -- Combining Reactive Planning and Bayesian Optimization to Solve Robotic Manipulation Tasks
Jonathan Styrud, Matthias Mayr, Erik Hellsten +2
Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks. While in the past, robot programs were often written statically and tuned ma…
Self-Correcting Bayesian Optimization through Bayesian Active Learning
Carl Hvarfner, Erik Hellsten, Frank Hutter +1
Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full poten…