Research Group "Stochastic Algorithms and Nonparametric Statistics"
Research Seminar "Mathematical Statistics" Winter Semester 2026/2027
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| 14.10.2026 | Dr. Chloé Rouyer (Universität Potsdam) |
| Derivative-free stochastic convex optimization in one dimension Stochastic convex optimization is a well understood problem when the learner has access to first-order feedback. However, when the learner can only access noisy function evaluations instead of the gradients, a logarithmic gap between upper and lower bounds has remained even in dimension one. In this talk, we will consider this problem of stochastic convex Derivative-Free Stochastic Convex Optimization in One Dimensionoptimization with zeroth-order feedback, present the tools that can be used instead of gradient and introduce a new adaptive algorithm which closes this logarithmic gap. |
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| 21.10.2026 | Prof. Dr. Kolyan Ray (Imperial College London) |
| Bayesian regression models with dense confounding variables Inferring causal relationships from observational data can be invalidated by the existence of confounding variables. We consider nonparametric Bayesian inference in regression models with additive errors under a dense confounding assumption, i.e. that every confounding variable affects many covariates. We show that by using suitable priors, which are equivalent to marginalizing out the confounders in a certain sense, such confounding can be corrected for. Our approach is illustrated via Gaussian process and spike and slab priors, for which we provide accompanying theoretical guarantees via posterior convergence rates. This is joint work with Luke Travis |
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| 28.10.2026 | Dr. Nicolas Verzelen (INRAE, Université Montpellier ) |
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| 04.11.2026 | Prof. Dr. Mark Podolski (Universität Luxemburg) | HVP 11 a, R.313 | |
| 11.11.2026 | Prof. Dr. Simon Weißmann (Universität Mannheim) |
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| 18.11.2026 | Prof. Dr. Botond Szabo (Bocconi University) |
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| 25.11.2026 | Dr. Christoph von Tycowicz (Zuse Institute Berlin (ZIB)) |
| Symmetry-preserving geodesic regression on Lie Groups for longitudinal medical imaging
Many medical imaging tasks require statistical modeling of continuous transformations, including longitudinal anatomical shape change and articulated skeletal motion. These transformations naturally live on Lie groups, where meaningful statistical analysis should respect group symmetries to remain invariant to arbitrary coordinate choices and reference frames. In this talk, I will present a geodesic regression framework on Lie groups for longitudinal imaging data. Common approaches rely on Riemannian metrics, but many Lie groups do not admit a metric fully compatible with the group structure. This mismatch breaks symmetry and leads to unstable regression estimates. We therefore introduce a non-metric, bi-invariant estimator that is equivariant under both left and right group actions. We evaluate the method on synthetic data and on an open-access clinical dataset of longitudinal knee joint configurations acquired for osteoarthritis research. The proposed approach yields stable trajectories and reproducible statistical conclusions, while state-of-the-art Riemannian methods exhibit sensitivity and instability. These results highlight the practical advantages of symmetry-preserving statistical modeling in longitudinal medical imaging studies. |
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| 02.12.2026 | Dr. Hugo Cui (Laboratory of Orsay |
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| 09.12.2026 | Prof. Dr. Yuta Koike (Tokyo University) |
| 16.12.2026 | Dr. Eardi Lila (University of Washington) |
| Geometric deep operator learning for inverse problems with functional data
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| 06.01.2027 | Prof. Dr. Claudia Strauch (Universiät Heidelberg) |
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| 13.01.2027 | |
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| 20.01.2027 | |
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| 27.01.2027 | TRR Workshop: Statistical Inference for Complex Stochastic Systems |
| HVP 11 a, R.313 |
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| 03.02.2027 | |
| 10.02.2027 | Prof. Dr. Alexander Kreiß (Universität Hildesheim) |
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| 17.02.2027 | |
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last reviewed: September 23, 2026 by Christine Schneider