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Jun 28, 2018
Machine Learning
Databases

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…

Automatic Exploration of Machine Learning Experiments on OpenML

Understanding the influence of hyperparameters on the performance of a machine learning algorithm is an important scientific topic in itself and can …
May 30, 2017
Computation

Boosting Functional Regression Models with FDboost

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…
Jan 18, 2019
Machine Learning
Computer Vision

Robust Anomaly Detection in Images using Adversarial Autoencoders

Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical i…
Apr 08, 2019
Machine Learning

Quantifying Model Complexity via Functional Decomposition for Better …

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While t…
Aug 25, 2019
Machine Learning
Artificial Intelligence

Tutorial and Survey on Probabilistic Graphical Model and Variational …

Probabilistic Graphical Models and Variational Inference play an important role in recent advances in Deep Reinforcement Learning. As a self-inclusiv…
Nov 06, 2019
Human-Computer Interaction
Artificial Intelligence

Towards Human Centered AutoML

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…
Feb 15, 2017
Machine Learning
Computation

Probing for sparse and fast variable selection with model-based boost…

We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to …
Apr 08, 2019
Machine Learning

Quantifying Interpretability of Arbitrary Machine Learning Models T…

To obtain interpretable machine learning models, either interpretable models are constructed from the outset - e.g. shallow decision trees, rule list…
Feb 13, 2018
Methodology

When and when not to use optimal model averaging

Traditionally model averaging has been viewed as an alternative to model selection with the ultimate goal to incorporate the uncertainty associated w…
Jul 01, 2019
Machine Learning

An Open Source AutoML Benchmark

In recent years, an active field of research has developed around automated machine learning (AutoML). Unfortunately, comparing different AutoML syst…
Mar 09, 2017
Machine Learning

mlrMBO: A Modular Framework for Model-Based Optimization of Expensive…

We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses th…
Dec 14, 2016
Methodology
Applications

Classification of Functional Data with k-Nearest-Neighbor Ensembles b…

During the last decades, many methods for the analysis of functional data including classification methods have been developed. Nonetheless, there ar…
Jun 07, 2019
Machine Learning

Variational Resampling Based Assessment of Deep Neural Networks under…

A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models…
Jun 08, 2015
Artificial Intelligence
Machine Learning

ASlib: A Benchmark Library for Algorithm Selection

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…
Apr 18, 2018
Machine Learning
Artificial Intelligence

Visualizing the Feature Importance for Black Box Models

In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models h…
Jul 11, 2018
Machine Learning

Automatic Gradient Boosting

Automatic machine learning performs predictive modeling with high performing machine learning tools without human interference. This is achieved by m…
Apr 08, 2019
Machine Learning

Sampling, Intervention, Prediction, Aggregation: A Generalized Framew…

Non-linear machine learning models often trade off a great predictive performance for a lack of interpretability. However, model agnostic interpretat…
May 04, 2018
Machine Learning
Computation
Methodology

Inference for $L_2$-Boosting

We propose a statistical inference framework for the component-wise functional gradient descent algorithm (CFGD) under normality assumption for model…
May 13, 2016
Computation

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…

Signal Regression Models for Location, Scale and Shape with an Appl…

We discuss scalar-on-function regression models where all parameters of the assumed response distribution can be modeled depending on covariates. We …
Aug 25, 2019
Machine Learning
Artificial Intelligence

Tutorial and Survey on Probabilistic Graphical Model and Variational …

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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