3 citations · 4 across the 3 of their papers we have counts for
6 papers · 1 filter
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
Jiarong Pan, Stefan Falkner, Felix Berkenkamp +1
Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged a…
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Matthias Feurer, Katharina Eggensperger, Stefan Falkner +2
Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial succe…
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
Matilde Gargiani, Aaron Klein, Stefan Falkner +1
We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based…
Learning to Design RNA
Frederic Runge, Danny Stoll, Stefan Falkner +1
Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved…
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
Arber Zela, Aaron Klein, Stefan Falkner +1
While existing work on neural architecture search (NAS) tunes hyperparameters in a separate post-processing step, we demonstrate that architectural choices and other hyperparameter…
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Stefan Falkner, Aaron Klein, Frank Hutter
Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimizatio…