David Rügamer

DeepAI AI Chat

Featured Co-authors

Bernd Bischl
81 publications
Marius Lindauer
45 publications
Christian Wachinger
42 publications
Nadja Klein
31 publications
Thomas Kneib
24 publications
Florian Pfisterer
21 publications
Giuseppe Casalicchio
20 publications
Andreas Bender
18 publications
Sebastian Pölsterl
17 publications
Sonja Greven
17 publications
Janek Thomas
16 publications

research

∙ 03/20/2023

Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis

While recent advances in large-scale foundational models show promising ...

0 Tobias Weber, et al. ∙

research

∙ 02/04/2023

mixdistreg: An R Package for Fitting Mixture of Experts Distributional Regression with Adaptive First-order Methods

This paper presents a high-level description of the R software package m...

0 David Rügamer, et al. ∙

research

∙ 11/24/2022

Estimating Conditional Distributions with Neural Networks using R package deeptrafo

Contemporary empirical applications frequently require flexible regressi...

0 Lucas Kook, et al. ∙

research

∙ 11/17/2022

Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods

In this work, we propose an efficient implementation of mixtures of expe...

0 David Rügamer, et al. ∙

research

∙ 11/04/2022

Uncertainty-aware predictive modeling for fair data-driven decisions

Both industry and academia have made considerable progress in developing...

0 Patrick Kaiser, et al. ∙

research

∙ 10/14/2022

Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models

Various privacy-preserving frameworks that respect the individual's priv...

0 Daniel Schalk, et al. ∙

research

∙ 08/31/2022

ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling

The autoregressive moving average (ARMA) model is a classical, and argua...

0 Philipp Schiele, et al. ∙

research

∙ 05/28/2022

Additive Higher-Order Factorization Machines

In the age of big data and interpretable machine learning, approaches ne...

0 David Rügamer, et al. ∙

research

∙ 05/25/2022

Factorized Structured Regression for Large-Scale Varying Coefficient Models

Recommender Systems (RS) pervade many aspects of our everyday digital li...

0 David Rügamer, et al. ∙

research

∙ 02/16/2022

Cross-Modal Common Representation Learning with Triplet Loss Functions

Common representation learning (CRL) learns a shared embedding between t...

18 Felix Ott, et al. ∙

research

∙ 02/14/2022

Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens

Handwriting is one of the most frequently occurring patterns in everyday...

2 Felix Ott, et al. ∙

research

∙ 02/12/2022

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 ...

0 Philipp Kopper, et al. ∙

research

∙ 11/09/2021

Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach

Europe was hit by several, disastrous heat and drought events in recent ...

0 Magdalena Mittermeier, et al. ∙

research

∙ 10/21/2021

Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation

The application of deep learning in survival analysis (SA) gives the opp...

0 Tobias Weber, et al. ∙

research

∙ 10/21/2021

Survival-oriented embeddings for improving accessibility to complex data structures

Deep learning excels in the analysis of unstructured data and recent adv...

0 Tobias Weber, et al. ∙

research

∙ 10/07/2021

Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization

Componentwise boosting (CWB), also known as model-based boosting, is a v...

0 Daniel Schalk, et al. ∙

research

∙ 09/12/2021

Automatic Componentwise Boosting: An Interpretable AutoML System

In practice, machine learning (ML) workflows require various different s...

0 Stefan Coors, et al. ∙

research

∙ 07/28/2021

Developing Open Source Educational Resources for Machine Learning and Data Science

Education should not be a privilege but a common good. It should be open...

7 Ludwig Bothmann, et al. ∙

research

∙ 04/06/2021

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

This paper describes the implementation of semi-structured deep distribu...

0 David Rügamer, et al. ∙

research

∙ 02/06/2021

Deep Semi-Supervised Learning for Time Series Classification

While Semi-supervised learning has gained much attention in computer vis...

0 Jann Goschenhofer, et al. ∙

research

∙ 01/03/2021

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany

During 2020, the infection rate of COVID-19 has been investigated by man...

10 Cornelius Fritz, et al. ∙

research

∙ 11/11/2020

Semi-Structured Deep Piecewise Exponential Models

We propose a versatile framework for survival analysis that combines adv...

12 Philipp Kopper, et al. ∙

research

∙ 10/15/2020

Deep Conditional Transformation Models

Learning the cumulative distribution function (CDF) of an outcome variab...

25 Philipp F. M. Baumann, et al. ∙

research

∙ 10/14/2020

Neural Mixture Distributional Regression

We present neural mixture distributional regression (NMDR), a holistic f...

8 David Rügamer, et al. ∙

research

∙ 07/15/2020

Selective Inference for Additive and Linear Mixed Models

This work addresses the problem of conducting valid inference for additi...

0 David Rügamer, et al. ∙

research

∙ 06/27/2020

A General Machine Learning Framework for Survival Analysis

The modeling of time-to-event data, also known as survival analysis, req...

22 Andreas Bender, et al. ∙

research

∙ 06/10/2020

A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction

Motivated by the application to German interest rates, we propose a time...

0 Christoph Berninger, et al. ∙

research

∙ 02/13/2020

A Unifying Network Architecture for Semi-Structured Deep Distributional Learning

We propose a unifying network architecture for deep distributional learn...

22 David Rügamer, et al. ∙

research

∙ 05/04/2018

Selective Inference for L_2-Boosting

We review several recently proposed post-selection inference frameworks ...

0 David Rügamer, et al. ∙

research

∙ 05/04/2018

Valid Inference for L_2-Boosting

We review several recently proposed post-selection inference frameworks ...

0 David Rügamer, et al. ∙

research

∙ 03/15/2018

Conditional Model Selection in Mixed-Effects Models with cAIC4

Model selection in mixed models based on the conditional distribution is...

0 Benjamin Säfken, et al. ∙

research

∙ 09/20/2016

Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals

The link between different psychophysiological measures during emotion e...

0 David Rügamer, et al. ∙