Forschungsgruppe "Stochastische Algorithmen und Nichtparametrische Statistik"

Seminar "Modern Methods in Applied Stochastics and Nonparametric Statistics" Winter Semester 2017/2018

  • Place: Weierstrass-Institute for Applied Analysis and Stochastics, Room 406 (4th floor), Mohrenstraße 39, 10117 Berlin
  • Time: Tuesdays, 3:00PM - 4:00PM
17.10.17 Dr. Alexandra Suvorikova (WIAS Berlin)
Two-sample test based on 2-Wasserstein distance
24.10.17

31.10.17 Reformationstag

07.11.17 Egor Klochkov (HU Berlin)
Invertibility of 1D random kernel matrix
14.11.17 Adrien Barrasso (École Nationale Supérieure de Techniques Avancées, Paris)
BSDEs and decoupled mild solutions of (possibly singular, path dependent or Integro-PDEs)
21.11.17 Valeriy Avanesov (WIAS Berlin)
Dynamics of high-dimensional covariance matrices
We consider the detection and localization of an abrupt break in the covariance structure of high-dimensional random data. The study proposes two novel approaches for this problem. The approaches are essentially hypothesis testing procedures which requires a proper choice of a critical level. In that regard calibration schemes, which are in turn different non-standard bootstrap procedures, are proposed. One of the approaches relies on techniques of inverse covariance matrix estimation, which is motivated by applications in neuroimaging. A limitation of the approach is a sparsity assumption crucial for precision matrix estimation which the second approach does not rely on. The description of the approaches are followed by a formal theoretical study justifying the proposed calibration schemes under mild assumptions and providing the guaranties for the break detection. Theoretical results for the first approach rely on the guaranties for inference of precision matrix procedures. Therefore, we rigorously justify adaptive inference procedures for precision matrices. All the results are obtained in a truly high- dimensional (dimensionality p _x001d_ n) finite-sample setting. The theoretical results are supported by simulation studies, most of which are inspired by either real- world neuroimaging or financial data.
28.11.17

05.12.17 Maxim Panov (Skolkovo Institute of Science and Technology, Moskow)
Consistent estimation of mixed memberships with successive projections
In this talk, we consider several types of accelerated randomized gradient methods: random directional search, random coordinate descent, randomized zero-order method. Using the concept of inexact oracle, we present a generic theorem on the convergence rate for all the three methods. Despite their random nature, these methods have complexity with the same dependence on the desired accuracy of the solution as deterministic accelerated gradient method. Joint work with A. Gasnikov and A. Tiurin.
12.12.17 Franz Besold (HU Berlin)
Persistence diagrams
In this talk we introduce persistence diagrams. These can be used as a tool to infer topological information from noisy data. To do that, we review simplicial and singular homology. Persistence diagrams have originally be defined for functions on topological spaces, but can be more generally defined using persistence modules. Stability results ensuring that close data sets have close persistence diagrams show that persistence diagrams are well-suited to deal with real-life data. Applications include data smoothing or recovering topological features of manifolds from a sampled point cloud.
19.12.17

02.01.17

09.01.17

16.01.17 Dr. Karsten Tabelow (WIAS Berlin)

23.01.17 Dr. Paolo Pigato (WIAS Berlin)
30.01.17 Benjamin Stemper (WIAS und TU Berlin)

06.02.17 Dr. Mario Maurelli (WIAS und TU Berlin)

13.02.17



last reviewed: November, 15, 2017, Christine Schneider