Research Group "Stochastic Algorithms and Nonparametric Statistics"

Seminar "Modern Methods in Applied Stochastics and Nonparametric Statistics" Winter Semester 2018/2019

  • Place: Weierstrass-Institute for Applied Analysis and Stochastics, Room 406 (4th floor), Mohrenstraße 39, 10117 Berlin
  • Time: Tuesdays, 3:00PM - 4:00PM
11.09.2018 Franz Besold (Humboldt-Universität zu Berlin)
Adaptive clustering using kernel density estimators
We investigate statistical properties of a clustering algorithm suggested by Ingo Steinwart, Bharath K. Sriperumbudur and Philipp Thomann that receives level set estimates from a kernel density estimator and then estimates the first split in the density level cluster tree if such a split is present or detects the absence of such a split. Key aspects of the analysis include finite sample guarantees, consistency, rates of convergence, and an adaptive data-driven strategy for choosing the kernel bandwidth. For the rates and the adaptivity we do not need continuity assumptions on the density such as Hölder continuity, but only require intuitive geometric assumptions of non-parametric nature.
18.09.2018 Prof. Thamban Nair (Indian Institute of Technology Madras)
An inverse problem in parabolic PDEs
25.09.2018
Seminar room no. 3.13 at HVP 11a
02.10.18

09.10.2018
Seminar room no. 3.13 at HVP 11a
16.10.18 Alexander Gasnikov (MIPT)
Unified view on accelerated methods for structural convex optimization problems
23.10.18 Alexey Naumov (Skoltech)
Gaussian approximations for maxima of large number of quadratic forms of high-dimensional random vectors
Let X_1, ... , X_n be i.i.d. random vectors taking values in R^d, d \geq 1, and Q_1, ... , Q_p, p \geq 1, be symmetric positive definite matrices. We consider the distribution function of vector (Q_j S_n, S_n), j = 1, ... , p, where S_n = n^{-1/2}(X_1 + ... + X_n), and prove the rate of Gaussian approximation with explicit dependence on n, p and d. We also compare this result with results of Bentkus (2003) and Chernozhukov, Chetverikov, Kato (2016). Applications to change point detection and model selection will be discussed as well. The talk is based on the joint project with F. Goetze, V. Spokoiny and A. Tikhomirov.
30.10.18
ESH Lecture room
06.11.18 First Talk John Meddocs (École Polytechnique Fédérale de Lausanne)
The Seminar starts at 2 p. m. Estimating structured precision matrices
In my group's work on estimating parameter sets in coarse-grain (or multi-scale) sequence-dependent models of DNA we came across the following basic problem: For a given (positive-definite) covariance matrix (in our case estimated in standard ways from Molecular dynamics simulation time series) what is the "best fit" precision, or inverse covariance, matrix under the contraint that the precision matrix must have a prescribed block banded sparsity pattern. I will describe various known, but apparently not well known, results in this and related directions, involving maximum entropy and relative entropy and likelihood principles.
06.11.18 Second Talk Raul Tempone (RWTH Aachen)
The talk starts at 3 p. m.
13.11.18
Seminar room no. 3.13 at HVP 11a
20.11.18 Michele Coghi (WIAS Berlin)

We investigate concentration of supremum of quadratic forms via entropy inequality. The problem is well understood for Gaussian vectors, and, more generally for K-concentrated vectors. We extend the result to vectors with indpenent sub-Gaussian entries with an extra logarythmic term. This is a joint work with Nikita Zhivotovskiy.
27.11.18 Yegor Klochkov (Humboldt Universität zu Berlin)
On uniform Hanson-Wright type inequalities for sub-Gaussian entries
We investigate concentration of supremum of quadratic forms via entropy inequality. The problem is well understood for Gaussian vectors, and, more generally for K-concentrated vectors. We extend the result to vectors with indpenent sub-Gaussian entries with an extra logarythmic term. This is a joint work with Nikita Zhivotovskiy.
04.12.18 Yangwen Sun (Humboldt Universität zu Berlin)
Complete graph based online change point detection
11.12.18 Mathias Staudigl (Maastricht University)

18.12.18



last reviewed: October 22, 2018, Christine Schneider