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Article
Editorial special issue: Bridging the gap between AI and Statistics
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Article
Open AccessMixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods
In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimization techniques with adapti...
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Living Reference Work Entry In depth
Methoden für die Analyse funktionaler Daten
Funktionale Daten entstehen als diskrete Messungen von inhärent glatten Funktionen wie z. B. Bewegungsprofilen oder Infrarot-Absorptionsspektren. Dieses Kapitel behandelt anhand konkreter Beispiele einige grun...
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Article
Open AccessProbabilistic time series forecasts with autoregressive transformation models
Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure that the model class used to...
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Chapter and Conference Paper
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
While recent advances in large-scale foundational computer vision models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we progress into th...
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Chapter and Conference Paper
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs
Generative models allow for the creation of highly realistic artificial samples, opening up promising applications in medical imaging. In this work, we propose a multi-stage encoder-based approach to invert th...
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Chapter and Conference Paper
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition
The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. The goal of domain adaptation (DA) is to miti...
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Chapter and Conference Paper
Factorized Structured Regression for Large-Scale Varying Coefficient Models
Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized r...
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Chapter and Conference Paper
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the...
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Article
Open AccessBenchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens
Handwriting is one of the most frequently occurring patterns in everyday life and with it comes challenging applications such as handwriting recognition, writer identification and signature verification. In co...
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Article
Open AccessMachine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain
Prognostic models play an important clinical role in the clinical management of neck pain disorders. No study has compared the performance of modern machine learning (ML) techniques, against more traditional r...
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Article
Open AccessCombining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool fo...
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Chapter and Conference Paper
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis
Survival analysis (SA) is an active field of research that is concerned with time-to-event outcomes and is prevalent in many domains, particularly biomedical applications. Despite its importance, SA remains ch...
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Article
Open AccessA novel metric of reliability in pressure pain threshold measurement
The inter-session Intraclass Correlation Coefficient (ICC) is a commonly investigated and clinically important metric of reliability for pressure pain threshold (PPT) measurement. However, current investigatio...
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Chapter and Conference Paper
A General Machine Learning Framework for Survival Analysis
The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, and that extend to settings wit...
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Chapter and Conference Paper
Deep Conditional Transformation Models
Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings. Conditional transformation models provi...
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Article
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 errors, also known as \(L_2\)L2-Boosting. The CFGD is one of t...
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Article
Open AccessGiardiosis and other enteropathogenic infections: a study on diarrhoeic calves in Southern Germany
Diarrhoea induces massive problems in the rearing of calves. The aim of the study was to obtain current data about the frequency of Giardia spp., Cryptosporidium spp. and Eimeria spp. in diarrhoeic calves in Sout...
