no code implementations • 5 Apr 2024 • Romain Egele, Felix Mohr, Tom Viering, Prasanna Balaprakash
To reach high performance with deep learning, hyperparameter optimization (HPO) is essential.
no code implementations • 2 Oct 2023 • Haozhe Sun, Isabelle Guyon, Felix Mohr, Hedi Tabia
It has become mainstream in computer vision and other machine learning domains to reuse backbone networks pre-trained on large datasets as preprocessors.
3 code implementations • NeurIPS 2022 • Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks.
1 code implementation • 16 Jan 2023 • Tanja Tornede, Alexander Tornede, Lukas Fehring, Lukas Gehring, Helena Graf, Jonas Hanselle, Felix Mohr, Marcel Wever
PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed to reduce the involved manual effort significantly.
no code implementations • 15 Jun 2022 • Adrian El Baz, Ihsan Ullah, Edesio Alcobaça, André C. P. L. F. Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Chaoyu Guan, Isabelle Guyon, Timothy Hospedales, Shell Hu, Mike Huisman, Frank Hutter, Zhengying Liu, Felix Mohr, Ekrem Öztürk, Jan N. van Rijn, Haozhe Sun, Xin Wang, Wenwu Zhu
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available.
no code implementations • 28 Jan 2022 • Felix Mohr, Jan N. van Rijn
Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e. g. the number of training examples or the number of training iterations.
1 code implementation • 29 Nov 2021 • Felix Mohr, Marcel Wever
An essential task of Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on a given dataset.
1 code implementation • 27 Nov 2021 • Felix Mohr, Jan N. van Rijn
Common cross-validation (CV) methods like k-fold cross-validation or Monte-Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a large portion of the given data and testing on the remaining data.
no code implementations • 10 Nov 2021 • Tanja Tornede, Alexander Tornede, Jonas Hanselle, Marcel Wever, Felix Mohr, Eyke Hüllermeier
Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool.
no code implementations • 10 Sep 2021 • Eyke Hüllermeier, Felix Mohr, Alexander Tornede, Marcel Wever
The notion of bounded rationality originated from the insight that perfectly rational behavior cannot be realized by agents with limited cognitive or computational resources.
no code implementations • ICML Workshop AutoML 2021 • Felix Mohr, Marcel Wever
Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset.
1 code implementation • ICML Workshop AutoML 2021 • Felix Mohr, Jan N. van Rijn
We run a large scale experiment on the 67 datasets from the AutoML benchmark, and empirically show that LCCV in over 90\% of the cases leads to similar performance (at most 0. 5\% difference) as 10-fold CV, but provides additional insights on the behaviour of a given model.
no code implementations • 18 Mar 2021 • Felix Mohr, Marcel Wever
Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset.
no code implementations • 2 Mar 2021 • Felix Mohr, Gonzalo Mejía, Francisco Yuraszeck
In this paper we study a simple extension of the total weighted flowtime minimization problem for single and identical parallel machines.
1 code implementation • 6 Jul 2020 • Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr, Eyke Hüllermeier
In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
no code implementations • 9 Nov 2018 • Marcel Wever, Felix Mohr, Eyke Hüllermeier
Automated machine learning (AutoML) has received increasing attention in the recent past.
1 code implementation • Machine Learning 2018 • Felix Mohr, Marcel Wever, Eyke Hüllermeier
Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset).