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Wed, 07. Oct at 16:30
IMoS 3003
Cones of submodular functions and quotients in Discrete Convex Analysis
Abstract. The set of submodular functions on a finite ground set forms a polyhedral cone. While its facets are easily described by a subset of the submodular inequalities, getting insights into the rays is much more difficult. I report on recent progress on the size of the rays and discuss bounds on their number. The latter uses ideas from quotients of submodular functions. I finish with an outlook on quotients more generally in Discrete Convex Analysis. This is based on joint works with Matthew Maat, Ben Smith, Germain Poullot, Arnau Padrol, Marie Brandenburg, Katarina Krivokuća.
Wed, 14. Oct at 10:00
ESH, Anton-Wilhel...
Wed, 14. Oct at 11:30
WIAS-406
Large Deviation Principle for Friendship Biases in Galton-Watson Trees
Abstract. The friendship bias of a vertex is defined as the difference between the average degree of its neighbours and its own degree; for isolated vertices, this bias is considered to be zero. According to the sign of the friendship bias, vertices can naturally be classified as "negative", "neutral", or "positive". The friendship paradox says that the average friendship bias is non-negative for all finite undirected graphs, whether simple graphs or multigraphs. However, the combined number of neutral and positive vertices can be significantly smaller than the number of negative vertices. For instance, in a complete graph on a large number of vertices with a single edge removed, only the two endpoints of the removed edge are positive, while all other vertices are negative. The fractions of different vertex types can also vary across graphs and reflect aspects of the geometry of a graph. The typical behaviour of these fractions has been studied for sparse random graphs that are locally tree-like, as well as for finite and infinite Galton-Watson trees. In this talk, we analyse the atypical behaviour of the fractions of vertex types along a random downward path in an infinite Galton-Watson tree by deriving a large deviation principle as the branching depth grows. The rate function is characterised through a variational problem involving relative entropy under a linear constraint. We discuss its properties in the case of binary branching. Based on joint work with Frank den Hollander.
Wed, 14. Oct at 14:15
WIAS, Erhard-Schm...
The variational solution of the Dirichlet problem for elliptic operators with complex lower-order coefficients
Abstract
Wed, 14. Oct at 16:30
IMoS 5004
Turán problems in the uniform setting
Abstract. Turán density is the minimum density that guarantees the existence of a given substructure. Almost all constructions that are known or conjectured to be extremal in the Turán density setting have highly non-uniform density distribution. This led Erdős and Sós to introduce the notion of the uniform Turán density, which requires the density of the host structure to be uniformly distributed. Erdős and Sós specifically asked to determine the uniform Turán densities of two 3-uniform hypergraphs: the Broken Tetrahedron and the Tetrahedron (K_4^{(3)-} and K_4^{(3)}). The case of the Broken Tetrahedron was resolved by Glebov, Volec and the speaker [Israel J. Math. 211 (2016), 349-366], and a computer-free proof was given by Reiher, Rödl and Schacht [J. Eur. Math. Soc. 97 (2018), 77–97]. The hypergraph regularity based approach of Reiher, Rödl and Schacht revolutionized the area and led to numerous results on the uniform Turán density of hypergraphs, which we survey during the talk. These include the spectacular result of Lamaison linking the uniform Turán densities and palette constructions, which are based on the lower bound method of Rödl. We conclude by reporting on a recent result of Kielak, Lamaison, Liu, Shu, Wu and the speaker, which resolves the Tetrahedron case and confirms that Rödl's lower bound construction from 1986 is optimal.
Thu, 15. Oct at 11:00
Room IMOS 2003
Hypocoercive Langevin Dynamics on the Lie Groups SE(2) and SE(3)
Abstract. We consider Langevin-type diffusions on Lie groups of rigid motions, where the dynamics couple position and orientation and the noise acts only in a subset of directions, leading to degenerate Kolmogorov generators. While hypocoercivity for related models in Euclidean settings is well understood, our aim is to develop an intrinsic formulation on the underlying Lie group and to identify the mechanisms responsible for exponential convergence to equilibrium. Starting from the planar motion group SE(2), we express the generator in terms of invariant vector fields and decompose it into its symmetric and antisymmetric parts. The natural projection onto the kernel of the symmetric part separates the microscopic and macroscopic dynamics, while averaging over the rotational subgroup leads to an effective elliptic operator in the spatial variables. This allows us to verify the microscopic and macroscopic coercivity properties required by an abstract hypocoercivity framework and to obtain exponential convergence to equilibrium. Building on this approach, we investigate the three-dimensional case SE(3), where the geometry and the corresponding operator structure become more involved. The results for SE(3) are currently in progress.
Thu, 15. Oct at 14:00
SR 115, Arnimallee 3
Organisatorial meeting: Overview and distribution of talks
Thu, 15. Oct at 16:15
HU Berlin, Instit...
Mean -Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study
Abstract. We study continuous-time mean-variance portfolio selection in markets where stock prices are diffusion processes driven by observable factors that are also diffusion processes yet the coefficients of these processes are unknown. Based on the recently developed reinforcement learning (RL) theory for diffusion processes, we present a general data-driven RL algorithm that learns the pre-committed investment strategy directly without attempting to learn or estimate the market coefficients. For multi-stock Black-Scholes markets without factors, we further devise a baseline algorithm and prove its performance guarantee by deriving a sublinear regret bound in terms of Sharpe ratio. For performance enhancement and practical implementation, we modify the baseline algorithm and carry out an extensive empirical study to compare their performance, in terms of a host of common metrics, with a large number of widely used portfolio allocation strategies on S&P 500 constituents. The results demonstrate that the proposed continuous-time RL strategy is consistently among the best especially in a volatile bear market, and decisively outperforms the model-based continuous-time counterparts by significant margins.
Thu, 15. Oct at 17:15
HU Berlin, Instit...
Mean -Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study
Abstract. We study continuous-time mean-variance portfolio selection in markets where stock prices are diffusion processes driven by observable factors that are also diffusion processes yet the coefficients of these processes are unknown. Based on the recently developed reinforcement learning (RL) theory for diffusion processes, we present a general data-driven RL algorithm that learns the pre-committed investment strategy directly without attempting to learn or estimate the market coefficients. For multi-stock Black-Scholes markets without factors, we further devise a baseline algorithm and prove its performance guarantee by deriving a sublinear regret bound in terms of Sharpe ratio. For performance enhancement and practical implementation, we modify the baseline algorithm and carry out an extensive empirical study to compare their performance, in terms of a host of common metrics, with a large number of widely used portfolio allocation strategies on S&P 500 constituents. The results demonstrate that the proposed continuous-time RL strategy is consistently among the best especially in a volatile bear market, and decisively outperforms the model-based continuous-time counterparts by significant margins.
Tue, 20. Oct at 13:15
Room 3.007, Rudow...
Wed, 21. Oct at 10:00
ESH, Anton-Wilhel...
Wed, 21. Oct at 16:30
IMoS 3003
Topology of real tropical hypersurfaces
Abstract. Real algebraic varieties seem to be quite distant from combinatorial geometry. However, it is possible to construct real algebraic hypersurfaces in projective spaces (and, more generally, in toric varieties) in a completely combinatorial fashion: one can patchwork them from pieces which essentially are hyperplanes. This procedure, called the combinatorial patchworking, is a particular case of the Viro method of construction of real algebraic varieties with prescribed topology. The combinatorial patchworking is one of the sources of tropical geometry, and the results of this construction can be seen as real tropical hypersurfaces. We will discuss the combinatorial patchworking and several results concerning the topology of real tropical hypersurfaces.
Thu, 22. Oct
WIAS, Erhard-Schm...
A variational characterization of a Gibbs measure related to the Aviles-Giga functional
Fri, 23. Oct at 14:15
Tue, 27. Oct at 11:15
1.023 (BMS Room, ...
Tue, 27. Oct at 14:00
MA 366
Wed, 28. Oct
Room 3.007, Rudow...
Wed, 28. Oct at 10:00
ESH, Anton-Wilhel...
Wed, 28. Oct at 11:30
WIAS-406
Wed, 28. Oct at 16:30
IMoS 3003
Orthogonal projections, linearization and sausages: a geometric approach to Cheeger sets
Abstract
Thu, 29. Oct at 16:15
HU Berlin, Instit...
Semi-static variance-optimal hedging with self-exciting jumps
Abstract. The aim of this talk is to investigate a quadratic, i.e., variance-optimal, semi-static hedging problem in an incomplete market model where the underlying log-asset price is driven by a diffusion process with stochastic volatility and a self-exciting jump process of Hawkes type. More precisely, we aim at hedging a claim at time T > 0 by using a portfolio of available contingent claims, so to minimize the variance of the residual hedging error at time T. In order to improve the replication of the claim, we look for a hybrid hedging strategy of semi-static type, in which some assets are continuously rebalanced (the dynamic hedging component) and for some other assets a buy-and-hold strategy (the static component) is performed. We discuss in detail a specific example in which the approach proposed is applied, i.e., a variance swap hedged by means of European options, and we provide a numerical illustration of the results obtained. (In cooperation with G. Callegaro, P. Di Tella and B. Ongarato, to appear on Mathematics of Operations Reasearch)
Tue, 03. Nov at 11:15
1.023 (BMS Room, ...
Refining Witten-Kontsevich
Abstract. The moduli space of metric Möbius graphs, which are non-orientable ribbon graphs, has one component homeomorphic to the moduli space of Riemann surfaces and another component homeomorphic to the moduli space of Klein surfaces. I'll discuss a lattice point count on this moduli space, weighted by a polynomial in b known as the measure of non-orientability. This "refined" lattice point count satisfies a refined version of Norbury's recursion for the count of lattice points on the moduli space of curves. Consequently, we obtain a recursion for the volumes of these moduli spaces that reduces to the Witten-Kontsevich recursion when b=0.
Tue, 03. Nov at 13:15
Room 3.007, Rudow...
Wed, 04. Nov at 10:00
ESH, Anton-Wilhel...
Wed, 04. Nov at 14:15
R. 405/406
Fri, 06. Nov at 14:15
Mathematics After AI
Tue, 10. Nov at 11:15
1.023 (BMS Room, ...
Tue, 10. Nov at 13:15
Room 3.007, Rudow...
Tue, 10. Nov at 14:00
MA 366
Wed, 11. Nov at 10:00
ESH, Anton-Wilhel...
Tue, 17. Nov at 11:15
1.023 (BMS Room, ...
Wed, 18. Nov at 10:00
ESH, Anton-Wilhel...
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 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.<br><br>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...
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
Fri, 27. Nov at 14:15
TU (IMoS 0005)
Tue, 01. Dec at 11:15
1.023 (BMS Room, ...
Wed, 02. Dec at 10:00
ESH, Anton-Wilhel...
Wed, 02. Dec at 10:00
Weierstrass-Insti...
Fri, 04. Dec at 14:30
Hamburg
Abstract
Fri, 04. Dec at 16:00
Hamburg
Abstract
Tue, 08. Dec at 11:15
1.023 (BMS Room, ...
Wed, 09. Dec at 10:00
ESH, Anton-Wilhel...
Wed, 09. Dec at 14:15
WIAS, Erhard-Schm...
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
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...
Fri, 08. Jan at 14:15
TU (IMoS 0005)
Tue, 12. Jan at 11:15
1.023 (BMS Room, ...
Wed, 13. Jan at 14:15
WIAS, Erhard-Schm...
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