Research Group Thomas Nagler
Thomas Nagler
Prof. Dr.
Principal Investigator
Thomas Nagler
is Professor of Computational Statistics & Data Science at LMU Munich.
His research is at the intersection of mathematical and computational statistics. He develops statistical methods, derives theoretical guarantees and scalable algorithms, packages them in user-friendly software, and collaborates with domain experts to solve problems in diverse areas.
Team members @MCML
PhD Students
Tobias Brock
→ Group Thomas Nagler
Computational Statistics & Data Science
Nicolai Palm
→ Group Thomas Nagler
Computational Statistics & Data Science
Recent News @MCML
Publications @MCML
2025
[29]
T. Vatter • T. Nagler
Throwing Vines at the Wall: Structure Learning via Random Search.
Preprint (Oct. 2025). arXiv
Throwing Vines at the Wall: Structure Learning via Random Search.
Preprint (Oct. 2025). arXiv
[28]
J. Herbinger • M. N. Wright • T. Nagler • B. Bischl • G. Casalicchio
Decomposing Global Feature Effects Based on Feature Interactions.
ECML-PKDD 2025 - Nectar Track at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Porto, Portugal, Sep 15-19, 2025. To be published. Preprint available. arXiv
Decomposing Global Feature Effects Based on Feature Interactions.
ECML-PKDD 2025 - Nectar Track at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Porto, Portugal, Sep 15-19, 2025. To be published. Preprint available. arXiv
[27]
H. Funk • R. Ludwig • H. Küchenhoff • T. Nagler
Towards more realistic climate model outputs: A multivariate bias correction based on zero-inflated vine copulas.
Journal of the Royal Statistical Society. Series C (Applied Statistics).qlaf044. Aug. 2025. DOI
Towards more realistic climate model outputs: A multivariate bias correction based on zero-inflated vine copulas.
Journal of the Royal Statistical Society. Series C (Applied Statistics).qlaf044. Aug. 2025. DOI
[26]
M. Arpogaus • T. Kneib • T. Nagler • D. Rügamer
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals.
UAI 2025 - 41st Conference on Uncertainty in Artificial Intelligence. Rio de Janeiro, Brazil, Jul 21-25, 2025. URL
[25]
R. Schulte • D. Rügamer • T. Nagler
Adjustment for Confounding using Pre-Trained Representations.
ICML 2025 - 42nd International Conference on Machine Learning. Vancouver, Canada, Jul 13-19, 2025. URL
Adjustment for Confounding using Pre-Trained Representations.
ICML 2025 - 42nd International Conference on Machine Learning. Vancouver, Canada, Jul 13-19, 2025. URL
[24]
N. Palm • H. Palm
PROBLEM-TAILORED MULTI-OBJECTIVE OPTIMIZATION ALGORITHM CONSTRUCTION BY PARETO REFLECTIONS.
Journal of Mathematical Sciences. Jul. 2025. DOI
PROBLEM-TAILORED MULTI-OBJECTIVE OPTIMIZATION ALGORITHM CONSTRUCTION BY PARETO REFLECTIONS.
Journal of Mathematical Sciences. Jul. 2025. DOI
[23]
E. Walter • T. Brock • P. Lahoud • N. Werner • F. Czaja • A. Tichy • C. Bumm • A. Bender • A. Castro • W. Teughels • F. Schwendicke • M. Folwaczny
Predictive modeling for step II therapy response in periodontitis - model development and validation.
npj Digital Medicine 8.445. Jul. 2025. DOI
Predictive modeling for step II therapy response in periodontitis - model development and validation.
npj Digital Medicine 8.445. Jul. 2025. DOI
[22]
T. Cheng • T. Vatter • T. Nagler • K. Chen
Vine Copulas as Differentiable Computational Graphs.
Preprint (Jun. 2025). arXiv
Vine Copulas as Differentiable Computational Graphs.
Preprint (Jun. 2025). arXiv
[21]
J. Min • H. Li • T. Nagler • S. Li
Assessing Climate-Driven Mortality Risk: A Stochastic Approach with Distributed Lag Non-Linear Models.
Preprint (Jun. 2025). arXiv
Assessing Climate-Driven Mortality Risk: A Stochastic Approach with Distributed Lag Non-Linear Models.
Preprint (Jun. 2025). arXiv
[20]
D. Dold • J. Kobialka • N. Palm • E. Sommer • D. Rügamer • O. Dürr
Paths and Ambient Spaces in Neural Loss Landscapes.
AISTATS 2025 - 28th International Conference on Artificial Intelligence and Statistics. Mai Khao, Thailand, May 03-05, 2025. URL
Paths and Ambient Spaces in Neural Loss Landscapes.
AISTATS 2025 - 28th International Conference on Artificial Intelligence and Statistics. Mai Khao, Thailand, May 03-05, 2025. URL
[19]
T. Nagler • T. Vatter
Solving Estimating Equations With Copulas.
AISTATS 2025 - 28th International Conference on Artificial Intelligence and Statistics. Mai Khao, Thailand, May 03-05, 2025. DOI
Solving Estimating Equations With Copulas.
AISTATS 2025 - 28th International Conference on Artificial Intelligence and Statistics. Mai Khao, Thailand, May 03-05, 2025. DOI
[18]
T. Nagler • D. Rügamer
Uncertainty Quantification for Prior-Fitted Networks using Martingale Posteriors.
AABI 2025 - 7th Symposium on Advances in Approximate Bayesian Inference collocated with the 13th International Conference on Learning Representations. Singapore, Apr 29, 2025. To be published. Preprint available. URL
Uncertainty Quantification for Prior-Fitted Networks using Martingale Posteriors.
AABI 2025 - 7th Symposium on Advances in Approximate Bayesian Inference collocated with the 13th International Conference on Learning Representations. Singapore, Apr 29, 2025. To be published. Preprint available. URL
[17]
T. Nagler • D. Rügamer
Uncertainty Quantification for Prior-Fitted Networks using Martingale Posteriors.
FPI @ICLR 2025 - Workshop on Frontiers in Probabilistic Inference: Learning meets Sampling at the 13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025. arXiv URL
Uncertainty Quantification for Prior-Fitted Networks using Martingale Posteriors.
FPI @ICLR 2025 - Workshop on Frontiers in Probabilistic Inference: Learning meets Sampling at the 13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025. arXiv URL
[16]
H. Funk • R. Ludwig • H. Küchenhoff • T. Nagler
Modelling Climate Variables at High Temporal Resolution.
Preprint (Feb. 2025). DOI
Modelling Climate Variables at High Temporal Resolution.
Preprint (Feb. 2025). DOI
[15]
H. Schulz-Kümpel • S. Fischer • T. Nagler • A.-L. Boulesteix • B. Bischl • R. Hornung
Constructing Confidence Intervals for 'the' Generalization Error – a Comprehensive Benchmark Study.
Journal of Data-centric Machine Learning Research 2.6. Jan. 2025. To be published. Preprint available. URL
Constructing Confidence Intervals for 'the' Generalization Error – a Comprehensive Benchmark Study.
Journal of Data-centric Machine Learning Research 2.6. Jan. 2025. To be published. Preprint available. URL
2024
[14]
T. Nagler • L. Schneider • B. Bischl • M. Feurer
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization.
NeurIPS 2024 - 38th Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 10-15, 2024. URL GitHub
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization.
NeurIPS 2024 - 38th Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 10-15, 2024. URL GitHub
[13]
M. Koshil • T. Nagler • M. Feurer • K. Eggensperger
Towards Localization via Data Embedding for TabPFN.
TLR @NeurIPS 2024 - 3rd Table Representation Learning Workshop at the 38th Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 10-15, 2024. URL
Towards Localization via Data Embedding for TabPFN.
TLR @NeurIPS 2024 - 3rd Table Representation Learning Workshop at the 38th Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 10-15, 2024. URL
[12]
J. Herbinger • M. N. Wright • T. Nagler • B. Bischl • G. Casalicchio
Decomposing Global Feature Effects Based on Feature Interactions.
Journal of Machine Learning Research 25.381. Dec. 2024. URL
Decomposing Global Feature Effects Based on Feature Interactions.
Journal of Machine Learning Research 25.381. Dec. 2024. URL
[11]
J. Gauss • T. Nagler
Asymptotics for estimating a diverging number of parameters -- with and without sparsity.
Preprint (Nov. 2024). arXiv
Asymptotics for estimating a diverging number of parameters -- with and without sparsity.
Preprint (Nov. 2024). arXiv
[10]
D. Rügamer • C. Kolb • T. Weber • L. Kook • T. Nagler
Generalizing orthogonalization for models with non-linearities.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Generalizing orthogonalization for models with non-linearities.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
[9]
D. Rundel • J. Kobialka • C. von Crailsheim • M. Feurer • T. Nagler • D. Rügamer
Interpretable Machine Learning for TabPFN.
xAI 2024 - 2nd World Conference on Explainable Artificial Intelligence. Valletta, Malta, Jul 17-19, 2024. DOI GitHub
Interpretable Machine Learning for TabPFN.
xAI 2024 - 2nd World Conference on Explainable Artificial Intelligence. Valletta, Malta, Jul 17-19, 2024. DOI GitHub
[8]
Y. Sale • P. Hofman • T. Löhr • L. Wimmer • T. Nagler • E. Hüllermeier
Label-wise Aleatoric and Epistemic Uncertainty Quantification.
UAI 2024 - 40th Conference on Uncertainty in Artificial Intelligence. Barcelona, Spain, Jul 16-18, 2024. URL
Label-wise Aleatoric and Epistemic Uncertainty Quantification.
UAI 2024 - 40th Conference on Uncertainty in Artificial Intelligence. Barcelona, Spain, Jul 16-18, 2024. URL
[7]
N. Palm • T. Nagler
An Online Bootstrap for Time Series.
AISTATS 2024 - 27th International Conference on Artificial Intelligence and Statistics. Valencia, Spain, May 02-04, 2024. URL
An Online Bootstrap for Time Series.
AISTATS 2024 - 27th International Conference on Artificial Intelligence and Statistics. Valencia, Spain, May 02-04, 2024. URL
2023
[6]
Y. Sale • P. Hofman • L. Wimmer • E. Hüllermeier • T. Nagler
Second-Order Uncertainty Quantification: Variance-Based Measures.
Preprint (Dec. 2023). arXiv
Second-Order Uncertainty Quantification: Variance-Based Measures.
Preprint (Dec. 2023). arXiv
[5]
J. Rodemann • J. Goschenhofer • E. Dorigatti • T. Nagler • T. Augustin
Approximately Bayes-optimal pseudo-label selection.
UAI 2023 - 39th Conference on Uncertainty in Artificial Intelligence. Pittsburgh, PA, USA, Jul 31-Aug 03, 2023. URL
Approximately Bayes-optimal pseudo-label selection.
UAI 2023 - 39th Conference on Uncertainty in Artificial Intelligence. Pittsburgh, PA, USA, Jul 31-Aug 03, 2023. URL
[4]
T. Nagler
Statistical Foundations of Prior-Data Fitted Networks.
ICML 2023 - 40th International Conference on Machine Learning. Honolulu, Hawaii, Jul 23-29, 2023. URL
Statistical Foundations of Prior-Data Fitted Networks.
ICML 2023 - 40th International Conference on Machine Learning. Honolulu, Hawaii, Jul 23-29, 2023. URL
[3]
T. Nagler • T. Vatter
Solving Estimating Equations With Copulas.
Journal of the American Statistical Association 119.546. Mar. 2023. DOI
Solving Estimating Equations With Copulas.
Journal of the American Statistical Association 119.546. Mar. 2023. DOI
2022
[2]
K. Lotto • T. Nagler • M. Radic
Modeling Stochastic Data Using Copulas for Applications in the Validation of Autonomous Driving.
Electronics 11.24. Dec. 2022. DOI
Modeling Stochastic Data Using Copulas for Applications in the Validation of Autonomous Driving.
Electronics 11.24. Dec. 2022. DOI
[1]
N. Palm • F. Stroebl • H. Palm
Parameter Individual Optimal Experimental Design and Calibration of Parametric Models.
IEEE Access 10. Oct. 2022. DOI GitHub
Parameter Individual Optimal Experimental Design and Calibration of Parametric Models.
IEEE Access 10. Oct. 2022. DOI GitHub
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