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Tue, 17. Nov at 11:15
1.023 (BMS Room, ...
Gauging symmetries and presympectic gPDEs
Abstract. Gauging symmetries provides a systematic way to construct new field theories and relate different formulations of their dynamics. In this talk, I will discuss two versions of this procedure: coupling a theory to background gauge fields and “flat gauging”, which introduces a topological sector BF-like sector without adding local degrees of freedom and can relate dual formulations. Both admit a natural geometric description in the framework of presymplectic gauge PDEs, which encodes local gauge theories and their Batalin–Vilkovisky formulation in graded geometric data. I will introduce the relevant structures and explain how the two gauging procedures arise as geometric constructions. Remarkably, this approach applies uniformly to symmetries of different degrees, including degree 1 symmetries (deformations), whose gauging promotes coupling constants to fields that are constant on shell. I will illustrate these constructions with some standard examples.
Tue, 17. Nov at 14:00
SR. 031, Arnimall...
Fadell–Neuwirth fibrations
Tue, 17. Nov at 14:00
MA 366
On the conjugacy problem in Aut(F_2) and related problems.
Abstract. The conjugacy problem is one of the central problems in computational group theory. Beyond determining whether the problem is solvable, one can seek to develop an algorithm that is paractical, in the sense that can be implemented on a computer. In this talk, we show that the methods proposed by Bogopolski for solving the conjugacy problem in the automorphism group of F_2 are practical. Furthermore, we present a new practical method for computing a generating set for the centralizer of a given automorphism.
Wed, 18. Nov at 10:00
ESH, Anton-Wilhel...
Robust Gaussian process regression: Posterior contraction
Abstract. Gaussian processes form a popular and flexible class of prior distributions with principled uncertainty quantification, but the Gaussian likelihood on which they rely makes them highly sensitive to outliers and contaminated observations. We study Gaussian process regression when among the n observations, m of these are possibly contaminated. A generic framework to obtain posterior contraction rates in this setting is considered, from a frequentist perspective as the sample size n and the number of corrupted observations m grow. We adopt a generalised Bayes approach in which the log-likelihood is replaced by a robustified version; we discuss various choices thereof, such as one recently proposed by Altamirano et al. (2024) that preserves conjugacy. We investigate the minimax rate of estimation under adversarial contamination in terms of the Sobolev smoothness β of the regression function and m. The question of building adaptive procedures to both β and m is investigated, and we show that a polynomial loss for adaptation must be paid in general. We instantiate our results for Matérn Gaussian process and deep ReLU network priors. Numerical experiments for existing and new robust algorithms are provided, illustrating the practical applicability of our results.
Wed, 18. Nov at 11:30
WIAS-406
Wed, 18. Nov at 14:15
WIAS, Erhard-Schm...
Perturbed minimizing movements of time-dependent functionals on metric spaces
Abstract
Thu, 19. Nov at 11:00
Room IMOS 2003
Stochastic Maximum Principles for Rough Stochastic Differential Equations
Abstract. We establish first- and second-order Pontryagin (a.k.a. stochastic) maximum principles for rough stochastic control problems with general nonlinear Lipγ -rough vector fields. This complements the dynamic programming approach of Friz–Lˆe–Zhang, and extends the maximum principle of Horst–Zhang beyond affine rough vector fields. We conclude with applications to pathwise stochastic optimal control.
Thu, 19. Nov at 14:00
SR 115, Arnimallee 3
Reasoning models and reinforcement learning
Thu, 19. Nov at 14:00
1.023 (BMS Room, ...
Near-critical dimers and sine-Gordon model
Abstract. We consider the dimer model with Temperleyan boundary conditions on the square lattice (or more generally isoradial superpositions). When the edge weights are critical it is well known since the work of Kenyon that in the scaling limit, the associated height function is conformally invariant and is described by the Gaussian free field (aka massless free boson). We consider here a massive perturbation of this model, where the edge weights deviate slightly from their critical values. I will discuss a recent result, obtained jointly with Scott Mason and Lucas Rey, in which we showed that the associated height function is described in the scaling limit by the sine-Gordon model at its free fermion point. In parallel, geometric interfaces naturally associated via Temperley's bijection were previously shown (in joint work with Levi Haunschmid-Sibitz) to converge to a massive variant of SLE (Schramm--Loewner evolution) introduced by Makarov and Smirnov. We will strive to present these ideas in a non-technical manner; in particular no probability background should be needed.
Tue, 24. Nov at 13:15
Room 3.007, Rudow...
Tue, 24. Nov at 14:00
SR. 031, Arnimall...
Cohomology of configurations of k points in R^n. I
Wed, 25. Nov at 10:00
ESH, Anton-Wilhel...
Symmetry-preserving geodesic regression on Lie Groups for longitudinal medical imaging
Abstract. 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.
Wed, 25. Nov at 11:30
WIAS-406
Universality of the Large-Scale Geometry for Planar Critical Random-Cluster Models
Abstract. Conformal invariance of general critical planar lattice models was conjectured by Polyakov in the 70s. Using the (non-rigorous) renormalisation group flow, one may predict that any scaling limit of such a model must be rotationally invariant. Since any such limit must also be translation and scale invariant, the argument goes that it will also be invariant under the action of the group of transformations which are locally compositions of these three types of transformations. This is exactly the conformal group, which, in the planar case, is infinite-dimensional and therefore, particularly rich as a symmetry group. Another conjecture of theoretical physics going back to Griffiths and Kadanoff is that of universality: That the scaling limits of various models with different microscopic details, e.g. the graphs on which they are defined, turn out to be the same across so-called universality classes. During the last 25 years, the first conjecture has received immense attention from the probabilistic community after Schramm's introduction of the SLE. In this talk, however, we shall turn our attention to the second question. Building on work by Duminil-Copin, Kozlowski, Krachun, Manolescu and Oulamara, we prove that the critical random-cluster models each satisfy a universality property across a large class of planar graphs including the hexagonal and triangular lattices. A consequence thereof will be that any scaling limit in one of these universality classes is rotationally invariant and thus, this may also be thought of as a stepping stone towards proving conformal invariance for all critical random-cluster models. Based on joint work with Ioan Manolescu.
Wed, 25. Nov at 16:30
IMoS 3003
Fast Counting of Cyclic Polytope Triangulations
Thu, 26. Nov at 14:00
SR 115, Arnimallee 3
Information geometry and gradient methods
Thu, 26. Nov at 15:15
Rudower Chaussee ...
Trust-Region Methods for Abs-Snooth Functions
Thu, 26. Nov at 16:15
HU Berlin, Instit...
Thu, 26. Nov at 17:15
HU Berlin, Instit...
Fri, 27. Nov at 14:15
@TU (IMoS 0005)
Mon, 30. Nov at 15:00
Rudower Chaussee ...
Asymptotic Approximation of Reactive Transport in a Bulk Domain with a Branched Thin Fracture
Tue, 01. Dec at 11:15
1.023 (BMS Room, ...
Review of shifted symplectic and Poisson geometry
Abstract. Shifted symplectic geometry is the theory of symplectic structures of various degrees, also allowing for derived intersections and stacky quotients. I will review motivations for this theory, and basic concepts, such as Lagrangian morphisms, intersections of Lagrangians and AKSZ theory.
Tue, 01. Dec at 11:15
1.023 (BMS Room, ...
Tue, 01. Dec at 14:00
SR. 031, Arnimall...
Cohomology of configurations of k points in R^n. II
Tue, 01. Dec at 14:00
MA 366
Wed, 02. Dec at 10:00
ESH, Anton-Wilhel...
Wed, 02. Dec at 10:00
Weierstrass-Insti...
Wed, 02. Dec at 11:30
WIAS-406
Thu, 03. Dec
WIAS, Erhard-Schm...
A variational characterization of a Gibbs measure related to the Aviles-Giga functional
Abstract
Thu, 03. Dec at 14:00
SR 115, Arnimallee 3
Fri, 04. Dec at 14:30
Geom H4, Hamburg
Arnold's chord conjecture in cotangent bundles
Abstract
Fri, 04. Dec at 16:00
Geom H4, Hamburg
Abstract
Tue, 08. Dec at 11:15
1.023 (BMS Room, ...
Tue, 08. Dec at 14:00
SR. 031, Arnimall...
Labeled configurations and scanning maps I
Tue, 08. Dec at 14:00
MA 366
Wed, 09. Dec at 10:00
ESH, Anton-Wilhel...
Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process
Abstract. We investigate sampling error bounds for denoising diffusion probabilistic models (DDPMs) in the 2-Wasserstein distance. The main contributions of this work are threefold: (i) Under general Lipschitz-type conditions on the score function and for a broad class of variance schedules, including the cosine schedule, we establish sharp upper bounds that are optimal in both the dimension and the number of steps, and recover several sharp error bounds previously obtained in the literature. (ii) We prove that the same Lipschitz-type conditions, which encompass those commonly imposed on the (learned) score, imply a logarithmic Sobolev inequality and hence a quadratic transportation cost inequality for the DDPM. As a consequence, in settings covered by existing work, an optimal Wasserstein bound, up to a logarithmic factor, follows from the recently obtained sharp error bound in the Kullback-Leibler divergence under geometric-type variance schedules. (iii) We show that for general log-concave target distributions, the optimal Wasserstein error bound remains attainable even without a quadratic transportation cost inequality for the target. Our analysis is based on viewing the DDPM sampler as a discretization of the Föllmer process rather than the conventional reverse Ornstein-Uhlenbeck process.
Wed, 09. Dec at 14:15
WIAS, Erhard-Schm...
Connecting models with data: inverse problems, their well-posedness and computational techniques
Abstract
Thu, 10. Dec at 16:15
HU Berlin, Instit...
Thu, 10. Dec at 17:15
HU Berlin, Instit...
Fri, 11. Dec at 14:15
@TU (IMoS 0005)
Tue, 15. Dec at 11:15
1.023 (BMS Room, ...
Wed, 16. Dec at 10:00
ESH, Anton-Wilhel...
Geometric deep operator learning for inverse problems with functional data
Wed, 16. Dec at 11:30
WIAS-406
Thu, 17. Dec at 14:00
SR 115, Arnimallee 3
Fri, 25. Dec
Christmas Day
Sat, 26. Dec
St. Stephen's Day
Fri, 01. Jan
New Year's Day
Wed, 06. Jan at 10:00
ESH, Anton-Wilhel...
Thu, 07. Jan at 14:00
SR 115, Arnimallee 3
Fri, 08. Jan at 14:15
@TU (IMoS 0005)
Tue, 12. Jan at 11:15
1.023 (BMS Room, ...
Tue, 12. Jan at 14:00
MA 366
Wed, 13. Jan at 10:00
Weierstrass-Insti...
Wed, 13. Jan at 14:15
WIAS, Erhard-Schm...
Thu, 14. Jan at 14:00
SR 115, Arnimallee 3
Tue, 19. Jan at 11:15
1.023 (BMS Room, ...
Wed, 20. Jan at 10:00
Weierstrass-Insti...
Wed, 20. Jan at 11:30
WIAS-406
Wed, 20. Jan at 16:30
IMoS 3003
Orthogonal projections, linearization and sausages: a geometric approach to Cheeger sets
Abstract
Thu, 21. Jan at 14:00
SR 115, Arnimallee 3
Introduction to critical spaces
Fri, 22. Jan at 14:15
@TU (IMoS 0005)
Tue, 26. Jan at 11:15
1.023 (BMS Room, ...
Wed, 27. Jan at 14:15
R. 405/406
Thu, 28. Jan at 17:15
HU Berlin, Instit...
Tue, 02. Feb at 11:15
1.023 (BMS Room, ...
Wed, 03. Feb at 11:30
WIAS-406
Thu, 04. Feb at 14:00
SR 115, Arnimallee 3
Wed, 10. Feb at 10:00
ESH, Anton-Wilhel...
Wed, 17. Feb at 10:00
Weierstrass-Insti...
Mon, 08. Mar
International Women's Day (Regional Holiday)
Fri, 26. Mar
Good Friday