Learning curves provide insight into the dependence of a learner's generalization
performance on the training set size. This important tool can be used for model selection, to …

Abstract Hyperparameter Optimization and Neural Architecture Search are powerful in
attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing …

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 …

Automated machine learning (AutoML) is a young research area aiming at making high-
performance machine learning techniques accessible to a broad set of users. This is …

When machine learning is outsourced to a rational agent, conflicts of interest might arise and
severely impact predictive performance. In this work, we propose a theoretical framework for …

Selecting a well-performing algorithm for a given task or dataset can be time-consuming and
tedious, but is crucial for the successful day-to-day business of developing new AI & ML …

To reach high performance with deep learning, hyperparameter optimization (HPO) is
essential. This process is usually time-consuming due to costly evaluations of neural …

Hyperparameter optimization (HPO) is crucial for fine-tuning machine learning models but
can be computationally expensive. To reduce costs, Multi-fidelity HPO (MF-HPO) leverages …

Arguably, a desirable feature of a learner is that its performance gets better with an
increasing amount of training data, at least in expectation. This issue has received renewed …

Plotting a learner's generalization performance against the training set size results in a so-
called learning curve. This tool, providing insight in the behavior of the learner, is also …