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David Rügamer
David Rügamer
Diğer adlarDavid Ruegamer, David Rugamer
Professor at LMU Munich, PI at Munich Center for Machine Learning
stat.uni-muenchen.de üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Conditional model selection in mixed-effects models with caic4
B Säfken, D Rügamer, T Kneib, S Greven
Journal of Statistical Software 99 (8), 1-30, 2021
1162021
Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany
C Fritz, E Dorigatti, D Rügamer
Scientific Reports 12 (1), 3930, 2022
892022
Giardiosis and other enteropathogenic infections: a study on diarrhoeic calves in Southern Germany
J Gillhuber, D Rügamer, K Pfister, MC Scheuerle
BMC research notes 7, 1-9, 2014
782014
Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain
BXW Liew, FM Kovacs, D Rügamer, A Royuela
European Spine Journal 31 (8), 2082-2091, 2022
732022
Boosting Functional Regression Models with FDboost
S Brockhaus, D Rügamer, S Greven
Journal of Statistical Software 94 (10), 2020
572020
Predictors of sudden cardiac death in doberman pinschers with dilated cardiomyopathy
L Klüser, PJ Holler, J Simak, G Tater, P Smets, D Rügamer, H Küchenhoff, ...
Journal of Veterinary Internal Medicine 30 (3), 722-732, 2016
562016
Semi-structured Distributional Regression
D Rügamer, C Kolb, N Klein
The American Statistician, 1-25, 2023
55*2023
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
T Papamarkou, M Skoularidou, K Palla, L Aitchison, J Arbel, D Dunson, ...
ICML 2024, 2024
52*2024
A General Machine Learning Framework for Survival Analysis
A Bender, D Rügamer, F Scheipl, B Bischl
ECML-PKDD 2020, 2020
382020
Interpretable machine learning models for classifying low back pain status using functional physiological variables
BXW Liew, D Rugamer, AM De Nunzio, D Falla
European Spine Journal 29 (8), 1845-1859, 2020
362020
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift
F Ott, D Rügamer, L Heublein, B Bischl, C Mutschler
ACM MM 2022, 2022
352022
FDboost: Boosting functional regression models
S Brockhaus, D Rügamer, A Stöcker
R package version 0.2-0, URL https://CRAN. R-project. org/package= FDboost, 2016
332016
Boosting factor-specific functional historical models for the detection of synchronisation in bioelectrical signals
D Rügamer, S Brockhaus, K Gentsch, K Scherer, S Greven
Journal of Royal Statistical Society: Series C, 2016
282016
Deep Conditional Transformation Models
P Baumann, T Hothorn, D Rügamer
ECML-PKDD 2021 12977, 2021
252021
Semi-Structured Deep Piecewise Exponential Models
P Kopper, S Pölsterl, C Wachinger, B Bischl, A Bender, D Rügamer
AAAI 2020, Spring Symposium on Survival Prediction -- Algorithms, Challenges …, 2020
252020
Inference for -Boosting
D Rügamer, S Greven
Statistics and Computing 30 (2), 279-289, 2020
252020
Deep Semi-Supervised Learning for Time Series Classification
J Goschenhofer, R Hvingelby, D Rügamer, J Thomas, M Wagner, B Bischl
ICMLA 2021, 2021
24*2021
Auxiliary cross-modal representation learning with triplet loss functions for online handwriting recognition
F Ott, D Rügamer, L Heublein, B Bischl, C Mutschler
IEEE access 11, 94148-94172, 2023
22*2023
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
T Weber, M Ingrisch, B Bischl, D Rügamer
PAKDD 2023, 2023
222023
A novel metric of reliability in pressure pain threshold measurement
B Liew, HY Lee, D Rügamer, AM De Nunzio, NR Heneghan, D Falla, ...
Scientific Reports 11 (1), 6944, 2021
222021
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