50 citations · 124 across the 14 of their papers we have counts for
15 papers
Credal Prediction based on Relative Likelihood
Timo Löhr, Paul Hofman, Felix Mohr +1
Predictions in the form of sets of probability distributions, so-called credal sets, provide a suitable means to represent a learner's epistemic uncertainty. In this paper, we prop…
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network
Haozhe Sun, Isabelle Guyon, Felix Mohr +1
It has become mainstream in computer vision and other machine learning domains to reuse backbone networks pre-trained on large datasets as preprocessors. Typically, the last layer…
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera +7
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open dat…
PyExperimenter: Easily distribute experiments and track results
Tanja Tornede, Alexander Tornede, Lukas Fehring +5
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…
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Adrian El Baz, Ihsan Ullah, Edesio Alcobaça +17
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 resourc…
Learning Curves for Decision Making in Supervised Machine Learning: A Survey
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 cert…
Naive Automated Machine Learning
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. This prob…
Fast and Informative Model Selection using Learning Curve Cross-Validation
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 la…
Towards Green Automated Machine Learning: Status Quo and Future Directions
Tanja Tornede, Alexander Tornede, Jonas Hanselle +3
Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine lea…
Automated Machine Learning, Bounded Rationality, and Rational Metareasoning
Eyke Hüllermeier, Felix Mohr, Alexander Tornede +1
The notion of bounded rationality originated from the insight that perfectly rational behavior cannot be realized by agents with limited cognitive or computational resources. Resea…
Naive Automated Machine Learning -- A Late Baseline for AutoML
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. AutoML has received enormou…
Single and Parallel Machine Scheduling with Variable Release Dates
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. While the standard problem simply defines…
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
Alexander Tornede, Marcel Wever, Stefan Werner +2
Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem…
Automated Multi-Label Classification based on ML-Plan
Marcel Wever, Felix Mohr, Eyke Hüllermeier
Automated machine learning (AutoML) has received increasing attention in the recent past. While the main tools for AutoML, such as Auto-WEKA, TPOT, and auto-sklearn, mainly deal wi…
Automated Machine Learning Service Composition
Felix Mohr, Marcel Wever, Eyke Hüllermeier
Automated service composition as the process of creating new software in an automated fashion has been studied in many different ways over the last decade. However, the impact of a…