David Rügamer

Orcid: 0000-0002-8772-9202

According to our database1, David Rügamer authored at least 65 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models.
Stat. Comput., February, 2024

Interpretable Additive Tabular Transformer Networks.
Trans. Mach. Learn. Res., 2024

Fusing structure from motion and simulation-augmented pose regression from optical flow for challenging indoor environments.
J. Vis. Commun. Image Represent., 2024

A Functional Extension of Semi-Structured Networks.
CoRR, 2024

Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects.
CoRR, 2024

How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression.
CoRR, 2024

Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.
CoRR, 2024

Training Survival Models using Scoring Rules.
CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
CoRR, 2024

Interpretable Machine Learning for TabPFN.
Proceedings of the Explainable Artificial Intelligence, 2024

Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Team MGTD4ADL at SemEval-2024 Task 8: Leveraging (Sentence) Transformer Models with Contrastive Learning for Identifying Machine-Generated Text.
Proceedings of the 18th International Workshop on Semantic Evaluation, 2024

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract).
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Generalizing Orthogonalization for Models with Non-Linearities.
Proceedings of the Forty-first International Conference on Machine Learning, 2024


Position: Why We Must Rethink Empirical Research in Machine Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Scalable Higher-Order Tensor Product Spline Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Bayesian Semi-structured Subspace Inference.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Probabilistic time series forecasts with autoregressive transformation models.
Stat. Comput., April, 2023

Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization.
J. Comput. Graph. Stat., April, 2023

deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.
J. Stat. Softw., 2023

Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany.
J. Comput. Graph. Stat., 2023

Unreading Race: Purging Protected Features from Chest X-ray Embeddings.
CoRR, 2023

Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models.
CoRR, 2023

Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization.
CoRR, 2023

Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition.
IEEE Access, 2023

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

A New PHO-rmula for Improved Performance of Semi-Structured Networks.
Proceedings of the International Conference on Machine Learning, 2023

Approximate Bayesian Inference with Stein Functional Variational Gradient Descent.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Frequentist Uncertainty Quantification in Semi-Structured Neural Networks.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens.
Int. J. Document Anal. Recognit., 2022

Selective inference for additive and linear mixed models.
Comput. Stat. Data Anal., 2022

Uncertainty-aware predictive modeling for fair data-driven decisions.
CoRR, 2022

ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling.
CoRR, 2022

Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression.
CoRR, 2022

Additive Higher-Order Factorization Machines.
CoRR, 2022

Cross-Modal Common Representation Learning with Triplet Loss Functions.
CoRR, 2022

Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Developing Open Source Educational Resources for Machine Learning and Data Science.
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, 2022

Factorized Structured Regression for Large-Scale Varying Coefficient Models.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift.
Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs.
Proceedings of the Medical Applications with Disentanglements - First MICCAI Workshop, 2022

Uncertainty-aware Evaluation of Time-series Classification for Online Handwriting Recognition with Domain Shift.
Proceedings of the 1st International Workshop on Spatio-Temporal Reasoning and Learning (STRL 2022) co-located with the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI 2022, 2022

Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition.
Proceedings of the Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, 2022

Joint Debiased Representation Learning and Imbalanced Data Clustering.
Proceedings of the IEEE International Conference on Data Mining Workshops, 2022

2021
Conditional Model Selection in Mixed-Effects Models with cAIC4.
J. Stat. Softw., 2021

Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach.
CoRR, 2021

Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation.
CoRR, 2021

Survival-oriented embeddings for improving accessibility to complex data structures.
CoRR, 2021

Transforming Autoregression: Interpretable and Expressive Time Series Forecast.
CoRR, 2021

Automatic Componentwise Boosting: An Interpretable AutoML System.
CoRR, 2021

Learning Statistical Representation with Joint Deep Embedded Clustering.
CoRR, 2021

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.
CoRR, 2021

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany.
CoRR, 2021

Semi-Structured Deep Piecewise Exponential Models.
Proceedings of AAAI Symposium on Survival Prediction, 2021

Deep Conditional Transformation Models.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Deep Semi-supervised Learning for Time Series Classification.
Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

2020
Inference for L<sub>2</sub>-Boosting.
Stat. Comput., 2020

Neural Mixture Distributional Regression.
CoRR, 2020

A Unifying Network Architecture for Semi-Structured Deep Distributional Learning.
CoRR, 2020

A General Machine Learning Framework for Survival Analysis.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2018
Valid Inference for L<sub>2</sub>-Boosting.
CoRR, 2018


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