65 citations · 159 across the 23 of their papers we have counts for
16 papers · 1 filter
Bayesian Optimization of CVaR with Unknown Hyperparameters
Valentin Margraf, Jonas Hanselle, Viktor Bengs +3
We study Bayesian optimization of the Conditional Value-at-Risk (CVaR) of an expensive black-box function that depends on a decision and an environmental variable. Existing no-regr…
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
Valentin Margraf, Marcel Wever, Sandra Gilhuber +3
In settings where only a budgeted amount of labeled data can be afforded, active learning seeks to devise query strategies for selecting the most informative data points to be labe…
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger +7
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to un…
Automated Machine Learning for Multi-Label Classification
Marcel Wever
Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For sup…
Iterative Deepening Hyperband
Jasmin Brandt, Marcel Wever, Dimitrios Iliadis +2
Hyperparameter optimization (HPO) is concerned with the automated search for the most appropriate hyperparameter configuration (HPC) of a parameterized machine learning algorithm.…
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets
David Schubert, Pritha Gupta, Marcel Wever
In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector t…
Hyperparameter optimization in deep multi-target prediction
Dimitrios Iliadis, Marcel Wever, Bernard De Baets +1
As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past dec…
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…
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…
Algorithm Selection on a Meta Level
Alexander Tornede, Lukas Gehring, Tanja Tornede +2
The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called inst…
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…
Towards Meta-Algorithm Selection
Alexander Tornede, Marcel Wever, Eyke Hüllermeier
Instance-specific algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidates most suitable for a specific instance of an algorithmic…
A Flexible Class of Dependence-aware Multi-Label Loss Functions
Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencia +2
Multi-label classification is the task of assigning a subset of labels to a given query instance. For evaluating such predictions, the set of predicted labels needs to be compared…
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…
Extreme Algorithm Selection With Dyadic Feature Representation
Alexander Tornede, Marcel Wever, Eyke Hüllermeier
Algorithm selection (AS) deals with selecting an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem, e.g., choosing…
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…