Understanding the influence of hyperparameters on the performance of a machine learning algorithm is an important scientific topic in itself and can help to improve automatic hyperparameter tuning procedures. Unfortunately, experimental meta data fo…

Understanding the influence of hyperparameters on the performance of a machine learning algorithm is an important scientific topic in itself and can …

The R add-on package FDboost is a flexible toolbox for the estimation of functional regression models by model-based boosting. It provides the possib…

Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical i…

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While t…

Probabilistic Graphical Models and Variational Inference play an important role in recent advances in Deep Reinforcement Learning. As a self-inclusiv…

Building models from data is an integral part of the majority of data science workflows. While data scientists are often forced to spend the majority…

We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to …

To obtain interpretable machine learning models, either interpretable models are constructed from the outset - e.g. shallow decision trees, rule list…

Traditionally model averaging has been viewed as an alternative to model selection with the ultimate goal to incorporate the uncertainty associated w…

In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML syst…

We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses th…

During the last decades, many methods for the analysis of functional data including classification methods have been developed. Nonetheless, there ar…

A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models…

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying perfo…

In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models h…

Automatic machine learning performs predictive modeling with high performing machine learning tools without human interference. This is achieved by m…

Non-linear machine learning models often trade off a great predictive performance for a lack of interpretability. However, model agnostic interpretat…

We propose a statistical inference framework for the component-wise functional gradient descent algorithm (CFGD) under normality assumption for model…

We discuss scalar-on-function regression models where all parameters of the assumed response distribution can be modeled depending on covariates. We thus combine signal regression models with generalized additive models for location, scale and shape…

We discuss scalar-on-function regression models where all parameters of the assumed response distribution can be modeled depending on covariates. We …

Probabilistic Graphical Modeling and Variational Inference play an important role in recent advances in Deep Reinforcement Learning. Aiming at a self…

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