Probabilistic Machine and Deep Learning

This group focuses on methodological and applied research in the context of probabilistc machine and deep learning. Topics include

Members

Name       Position
Prof. Dr. David Rügamer       Lead
Dr. Andreas Bender       PostDoc
Dr. Ludwig Bothmann       PostDoc
Dr. Mina Rezaei       PostDoc
Martin Binder       PhD Student
Lukas Burk       PhD Student
Daniel Dold       PhD Student
Emilio Dorigatti       PhD Student
Jann Goschenhofer       PhD Student
Hüseyin Anil Gündüz       PhD Student
Florian Karl       PhD Student
Chris Kolb       PhD Student
Philipp Kopper       PhD Student
Felix Ott       PhD Student
Tobias Pielok       PhD Student
Katharina Rath       PhD Student
Theresa Stüber       PhD Student
Tobias Weber       PhD Student
Lisa Wimmer       PhD Student

Publications

  1. Kolb C, Müller CL, Bischl B, Rügamer D (2023) Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. arXiv preprint arXiv:2307.03571.
    link|pdf    .
  2. Kolb C, Bischl B, Müller CL, Rügamer D (2023) Sparse Modality Regression Proceedings of the 37th International Workshop on Statistical Modelling, IWSM 2023,
    link|pdf    .
  3. Wiese JG, Wimmer L, Papamarkou T, Bischl B, Günnemann S, Rügamer D (2023) Towards Efficient Posterior Sampling in Deep Neural Networks via Symmetry Removal Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
    link|pdf    .
  4. Rügamer D (2023) A New PHO-rmula for Improved Performance of Semi-Structured Networks. ICML 2023.
  5. Weber T, Ingrisch M, Bischl B, Rügamer D (2023) Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference, PAKDD 2023,
    link|pdf    .
  6. Ott F, Raichur NL, Rügamer D, Feigl T, Neumann H, Bischl B, Mutschler C (2023) Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. arXiv:2208.00919.
    link|pdf    .
  7. Pielok T, Bischl B, Rügamer D (2023) Approximate Bayesian Inference with Stein Functional Variational Gradient Descent International Conference on Learning Representations,
    link|pdf    .
  8. Dorigatti E, Bischl B, Rügamer D (2023) Frequentist Uncertainty Quantification in Semi-Structured Neural Networks International Conference on Artificial Intelligence and Statistics, PMLR.
  9. Wimmer L, Sale Y, Hofman P, Bischl B, Hüllermeier E (2023) Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023),
    link|pdf    .
  10. Rügamer D, Kolb C, Klein N (2022) Semi-Structured Distributional Regression. The American Statistician.
    link|pdf    .
  11. Kook L, Baumann PFM, Dürr O, Sick B, Rügamer D (2022) Estimating Conditional Distributions with Neural Networks using R package deeptrafo. arXiv preprint arXiv:2211.13665.
    link|pdf    .
  12. Rügamer D, Pfisterer F, Bischl B, Grün B (2022) Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods. arXiv preprint arXiv:2211.09875.
    link|pdf    .
  13. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Representation Learning for Tablet and Paper Domain Adaptation in favor of Online Handwriting Recognition MPRSS 2022,
  14. Ziegler I, Ma B, Nie E, Bischl B, Rügamer D, Schubert B, Dorigatti E (2022) What cleaves? Is proteasomal cleavage prediction reaching a ceiling? NeurIPS 2022 Workshop on Learning Meaningful Representations of Life (LMRL),
    link|pdf    .
  15. Kaiser P, Rügamer D, Kern C (2022) Uncertainty as a key to fair data-driven decision making NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML),
    link|pdf    .
  16. Schalk D, Bischl B, Rügamer D (2022) Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models. arXiv preprint arXiv:2210.07723.
    link|pdf    .
  17. Schiele P, Berninger C, Rügamer D (2022) ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling. arXiv preprint arXiv:2208.14919.
    link|pdf    .
  18. Rath K, Rügamer D, Bischl B, Toussaint U von, Rea C, Maris A, Granetz R, Albert C (2022) Data augmentation for disruption prediction via robust surrogate models. Journal of Plasma Physics.
  19. Mittermeier M, Weigert M, Rügamer D, Küchenhoff H, Ludwig R (2022) A Deep Learning Version of Hess & Brezowskys Classification of Großwetterlagen over Europe: Projection of Future Changes in a CMIP6 Large Ensemble. Environmental Research Letters.
  20. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift ACM Multimedia,
    link|pdf    .
  21. Rügamer D, Bender A, Wiegrebe S, Racek D, Bischl B, Müller C, Stachl C (2022) Factorized Structured Regression for Large-Scale Varying Coefficient Models Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
    link|pdf    .
  22. Klaß A, Lorenz S, Lauer-Schmaltz M, Rügamer D, Bischl B, Mutschler C, Ott F (2022) Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift IJCAI-ECAI 2022, 1st International Workshop on Spatio-Temporal Reasoning and Learning,
  23. Fritz C, Nicola GD, Günther F, Rügamer D, Rave M, Schneble M, Bender A, Weigert M, Brinks R, Hoyer A, Berger U, Küchenhoff H, Kauermann G (2022) Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany. Journal of Computational & Graphical Statistics.
  24. Rügamer D (2022) Additive Higher-Order Factorization Machines. arXiv preprint arXiv:2205.14515.
    link|pdf    .
  25. Rügamer D, Kolb C, Fritz C, Pfisterer F, Kopper P, Bischl B, Shen R, Bukas C, Sousa LB de Andrade e, Thalmeier D, Baumann P, Kook L, Klein N, Müller CL (2022) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. Journal of Statistical Software (provisionally accepted).
    link|pdf    .
  26. Fritz C, Dorigatti E, Rügamer D (2022) Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. Scientific Reports 12, 2045–2322.
    link|pdf    .
  27. Rügamer D, Baumann P, Greven S (2022) Selective Inference for Additive and Mixed Models. Computational Statistics and Data Analysis 167, 107350.
    link|pdf    .
  28. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Cross-Modal Common Representation Learning with Triplet Loss Functions. arXiv preprint arXiv:2202.07901.
    link|pdf    .
  29. Dorigatti E, Goschenhofer J, Schubert B, Rezaei M, Bischl B (2022) Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. arXiv preprint arXiv:2109.05232.
    link|pdf    .
  30. Kopper P, Wiegrebe S, Bischl B, Bender A, Rügamer D (2022) DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis Advances in Knowledge Discovery and Data Mining, pp. 249–261. Springer International Publishing.
    link|pdf    .
  31. Liew BXW, Rügamer D, Duffy K, Taylor M, Jackson J (2021) The mechanical energetics of walking across the adult lifespan. PloS one 16, e0259817.
    link    .
  32. Mittermeier M, Weigert M, Rügamer D (2021) Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach. NeurIPS 2021, Tackling Climate Change with Machine Learning.
    link|pdf    .
  33. Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D (2021) Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021 Workshops, Bridging the Gap: From Machine Learning Research to Clinical Practice.
    link|pdf    .
  34. Weber T, Ingrisch M, Bischl B, Rügamer D (2021) Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021 Workshops, Deep Generative Models and Downstream Applications.
    link|pdf    .
  35. Liew BXW, Rügamer D, Zhai XJ, Morris S, Netto K (2021) Comparing machine, deep, and transfer learning in predicting joint moments in running. Journal of Biomechanics.
  36. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2021) 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 (WACV),
  37. Goschenhofer J, Hvingelby R, Rügamer D, Thomas J, Wagner M, Bischl B (2021) Deep Semi-Supervised Learning for Time Series Classification 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
    link | pdf    .
  38. Falla D, Devecchi V, Jimenez-Grande D, Rügamer D, Liew B (2021) Modern Machine Learning Approaches Applied in Spinal Pain Research. Journal of Electromyography and Kinesiology.
  39. Berninger C, Stöcker A, Rügamer D (2021) A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction. Journal of Forecasting.
    link|pdf    .
  40. Baumann PFM, Hothorn T, Rügamer D (2021) Deep Conditional Transformation Models Machine Learning and Knowledge Discovery in Databases. Research Track, pp. 3–18. Springer International Publishing.
    link|pdf    .
  41. Rath K, Albert CG, Bischl B, Toussaint U von (2021) Symplectic Gaussian process regression of maps in Hamiltonian systems. Chaos: An Interdisciplinary Journal of Nonlinear Science 31, 053121.
    link    .
  42. Kopper P, Pölsterl S, Wachinger C, Bischl B, Bender A, Rügamer D (2021) Semi-Structured Deep Piecewise Exponential Models. In: In: Greiner R , In: Kumar N , In: Gerds TA , In: Schaar M van der (eds) Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, pp. 40–53. PMLR.
    link|pdf    .
  43. Bender A, Rügamer D, Scheipl F, Bischl B (2021) A General Machine Learning Framework for Survival Analysis. In: In: Hutter F , In: Kersting K , In: Lijffijt J , In: Valera I (eds) Machine Learning and Knowledge Discovery in Databases, pp. 158–173. Springer International Publishing.
    link | pdf    .
  44. Günther F, Bender A, Katz K, Küchenhoff H, Höhle M (2020) Nowcasting the COVID-19 pandemic in Bavaria. Biometrical Journal.
    link|pdf    .
  45. Liew BXW, Peolsson A, Rügamer D, Wibault J, Löfgren H, Dedering A, Zsigmond P, Falla D (2020) Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy – a machine learning approach. Scientific Reports.
    link    .
  46. Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression. arXiv:2010.06889 [cs, stat].
    link|pdf    .
  47. Bender A, Python A, Lindsay SW, Golding N, Moyes CL (2020) Modelling geospatial distributions of the triatomine vectors of Trypanosoma cruzi in Latin America. PLOS Neglected Tropical Diseases 14, e0008411.
    link|pdf    .
  48. Beggel L, Pfeiffer M, Bischl B (2020) Robust Anomaly Detection in Images using Adversarial Autoencoders Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 206–222. Springer.
    link | pdf    .
  49. Pfister FMJ, Um TT, Pichler DC, Goschenhofer J, Abedinpour K, Lang M, Endo S, Ceballos-Baumann AO, Hirche S, Bischl B, others (2020) High-Resolution Motor State Detection in parkinson’s Disease Using convolutional neural networks. Scientific reports 10, 1–11.
    link    .
  50. Stachl C, Au Q, Schoedel R, Gosling SD, Harari GM, Buschek D, Völkel ST, Schuwerk T, Oldemeier M, Ullmann T, others (2020) Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences.
    link | pdf    .
  51. Rügamer D, Greven S (2020) Inference for L2-Boosting. Statistics and Computing 30, 279–289.
    link|pdf    .
  52. Sun X, Bischl B (2019) Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning 2019 IEEE Symposium Series on Computational Intelligence (SSCI),
    link|pdf    .
  53. Pfisterer F, Beggel L, Sun X, Scheipl F, Bischl B (2019) Benchmarking time series classification – Functional data vs machine learning approaches. arXiv preprint arXiv:1911.07511.
    link | pdf    .
  54. Beggel L, Kausler BX, Schiegg M, Pfeiffer M, Bischl B (2019) Time series anomaly detection based on shapelet learning. Computational Statistics 34, 945–976.
    link | pdf    .
  55. Sun X, Wang Y, Gossmann A, Bischl B (2019) Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. CoRR abs/1906.02972.
    link | pdf    .
  56. Goschenhofer J, Pfister FMJ, Yuksel KA, Bischl B, Fietzek U, Thomas J (2019) Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning. Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, 400–415.
    link | pdf    .
  57. Sun X, Gossmann A, Wang Y, Bischt B (2019) Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1344–1353.
    link|pdf    .
  58. Rügamer D, Greven S (2018) Selective inference after likelihood-or test-based model selection in linear models. Statistics & Probability Letters 140, 7–12.
  59. Rueger S, Goschenhofer J, Nath A, Firsching M, Ennen A, Bischl B (2022) Deep-Learning-based Aluminum Sorting on Dual Energy X-Ray Transmission Data. Sensor-based Sorting and Control.
    link|pdf    .

Contact

Feel free to contact us if you are looking for collaborations:

david.ruegamer [at] stat.uni-muenchen.de