4 citations · 12 across the 4 of their papers we have counts for
6 papers
Squirrel: A Switching Hyperparameter Optimizer
Noor Awad, Gresa Shala, Difan Deng +9
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge. Motivated by the fact that different optimizers work well on different problems, our approach swit…
Neural Model-based Optimization with Right-Censored Observations
Katharina Eggensperger, Kai Haase, Philipp Müller +2
In many fields of study, we only observe lower bounds on the true response value of some experiments. When fitting a regression model to predict the distribution of the outcomes, w…
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
Marius Lindauer, Matthias Feurer, Katharina Eggensperger +2
Bayesian Optimization (BO) is a common approach for hyperparameter optimization (HPO) in automated machine learning. Although it is well-accepted that HPO is crucial to obtain well…
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
Marius Lindauer, Katharina Eggensperger, Matthias Feurer +4
Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a s…
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger +2
We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We us…
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
Katharina Eggensperger, Marius Lindauer, Holger H. Hoos +2
The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard com…