David Rügamer

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Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerifierad e-postadress på stat.uni-muenchen.de
Sonja GrevenChair of Statistics, Humboldt-Universität zu BerlinVerifierad e-postadress på hu-berlin.de
Christopher MutschlerDirector Positioning & Networks, Fraunhofer IISVerifierad e-postadress på iis.fraunhofer.de
Thomas KneibChair of Statistics, Georg-August-University GöttingenVerifierad e-postadress på uni-goettingen.de
Vincent FortuinPrincipal Investigator, Helmholtz AI & TU MunichVerifierad e-postadress på tum.de
Tim G. J. RudnerFaculty Fellow, New York UniversityVerifierad e-postadress på nyu.edu
Almond StöckerPostDoc, École Polytechnic Fédéral de Lausanne (EPFL)Verifierad e-postadress på epfl.ch
Torsten HothornVerifierad e-postadress på R-project.org
Christian L. MüllerHelmholtz Munich; Ludwig-Maximilians-Universität München; Flatiron Institute, New YorkVerifierad e-postadress på helmholtz-munich.de
Andrew Gordon WilsonNew York UniversityVerifierad e-postadress på cims.nyu.edu
Philipp HennigUniversity of TübingenVerifierad e-postadress på uni-tuebingen.de
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerifierad e-postadress på cam.ac.uk
Agustinus KristiadiPostdoc, Vector InstituteVerifierad e-postadress på vectorinstitute.ai
Lucas KookAssistant Professor of Statistics, Vienna University of Economics and BusinessVerifierad e-postadress på wu.ac.at
Mohammad Emtiyaz KhanCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerifierad e-postadress på postman.riken.jp
Matthias FeurerLMU MunichVerifierad e-postadress på stat.uni-muenchen.de
Stephan GünnemannProfessor of Computer Science, Technical University of MunichVerifierad e-postadress på in.tum.de
Oliver DürrProfessor of Data Science, Konstanz University of Applied SciencesVerifierad e-postadress på htwg-konstanz.de
Eyke HüllermeierProfessor of Computer Science, Paderborn UniversityVerifierad e-postadress på upb.de
Thomas NaglerLMU Munich, Munich Center for Machine LearningVerifierad e-postadress på lmu.de

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David Rügamer

Andra namnDavid Ruegamer, David Rugamer

Professor at LMU Munich, PI at Munich Center for Machine Learning

Verifierad e-postadress på stat.uni-muenchen.de - Startsida

Deep Learning Uncertainty Quantification Optimization Statistics


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
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	116	2021
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	88	2022
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	78	2014
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	72	2022
Boosting Functional Regression Models with FDboost S Brockhaus, D Rügamer, S Greven Journal of Statistical Software 94 (10), 2020	56	2020
Semi-structured Distributional Regression D Rügamer, C Kolb, N Klein The American Statistician, 1-25, 2023	55*	2023
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	55	2016
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	47*	2024
A General Machine Learning Framework for Survival Analysis A Bender, D Rügamer, F Scheipl, B Bischl ECML-PKDD 2020, 2020	38	2020
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	36	2020
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift F Ott, D Rügamer, L Heublein, B Bischl, C Mutschler ACM MM 2022, 2022	35	2022
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	33	2016
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	28	2016
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	25	2020
Inference for -Boosting D Rügamer, S Greven Statistics and Computing 30 (2), 279-289, 2020	25	2020
Deep Conditional Transformation Models P Baumann, T Hothorn, D Rügamer ECML-PKDD 2021 12977, 2021	24	2021
Deep Semi-Supervised Learning for Time Series Classification J Goschenhofer, R Hvingelby, D Rügamer, J Thomas, M Wagner, B Bischl ICMLA 2021, 2021	23*	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	21	2023
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression D Rügamer, C Kolb, C Fritz, F Pfisterer, B Bischl, R Shen, C Bukas, ... Journal of Statistical Software 105 (2), 1-31, 2023	21*	2023

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