113 citations · 294 across the 24 of their papers we have counts for
5 papers · 1 filter
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek
Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions w…
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Neeratyoy Mallik, Edward Bergman, Carl Hvarfner +5
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been develo…
Out-of-Distribution Detection for Adaptive Computer Vision
Simon Kristoffersson Lind, Rudolph Triebel, Luigi Nardi +1
It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions. This paper proposes a method to adapt camera parameters according to a…
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applicati…
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…