We introduce ordered transfer hyperparameter optimisation (OTHPO), a ver...
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely ...
To achieve peak predictive performance, hyperparameter optimization (HPO...
The decoding of brain signals recorded via, e.g., an electroencephalogra...
Bayesian Optimization (BO) is a successful methodology to tune the
hyper...
Bayesian optimization (BO) is a sample efficient approach to automatical...
Bayesian optimization (BO) is among the most effective and widely-used
b...
We introduce a model-based asynchronous multi-fidelity hyperparameter
op...
We propose probabilistic models that can extrapolate learning curves of
...
Despite the recent progress in hyperparameter optimization (HPO), availa...
Due to the high computational demands executing a rigorous comparison be...
Recent advances in neural architecture search (NAS) demand tremendous
co...
While existing work on neural architecture search (NAS) tunes hyperparam...
Modern deep learning methods are very sensitive to many hyperparameters,...
Recent work has shown that optical flow estimation can be formulated as ...
We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampli...
Bayesian optimization has become a successful tool for hyperparameter
op...