Research Group "Stochastic Algorithms and Nonparametric Statistics"
Seminar "Modern Methods in Applied Stochastics and Nonparametric Statistics" SS 2020
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| 31.03.2020 | Dr. Karsten Tabelow (WIAS Berlin) |
| Model-based geometry reconstruction (MGBR) from TEM |
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| 07.04.2020 | Prof. Dr. Vladimir Spokoiny (WIAS Berlin) |
| Bayesian inference for nonlinear inverse problems
I will discuss some general results for the posterior in a nonlinear inverse problem with particular focus on nonlinear regression and Gaussian mixture models. |
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| 14.04.2020 | Dr. Alexandra Suvorikova (WIAS Berlin) |
| On some general pricniples behind the results on the bootstrap validity in Bures-Wasserstein space
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| 21.04.2020 | Dr. Nikita Zhivotovskiy (Google Research) |
| Fast classification rates in online and statistical learning with abstention
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| 28.04.2020 | Darina Dvinskikh (WIAS Berlin) |
| SA vs SAA for population Wasserstein barycenter calculation | |
| 05.05.2020 | Dr. Oleg Butkovsky (WIAS Berlin) |
| Stochastic sewing with controls and skew fractional Brownian motion I will explain how sewing with controls developed by Peter Friz & Huilin Zhang can be extended to the stochastic setup and show how this can be used to establish existence of skew fractional brownian motion and related SDEs with iregular drift. | |
| 12.05.2020 | Dr. César A. Uribe (Massachusetts Institute of Technology) |
| Distributed inference for cooperative learning We study the problem of cooperative inference where a group of agents interact over a network and seek to estimate a joint parameter that best explains a set of observations. Agents do not know the network topology or the observations of other agents. We explore a variational interpretation of the Bayesian posterior density, and its relation to the stochastic mirror descent algorithm, to propose a new distributed learning algorithm. We show that, under appropriate assumptions, the beliefs generated by the proposed algorithm concentrate around the true parameter exponentially fast. We provide explicit non-asymptotic bounds for the convergence rate. Moreover, we develop explicit and computationally efficient algorithms for observation models belonging to exponential families. |
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| 19.05.2020 | Priv.- Doz. Dr. John G. M. Schoenmakers (WIAS Berlin) |
| Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithm In this talk we propose a Weighted Stochastic Mesh (WSM) algorithm for approximating the value of discrete and continuous time optimal stopping problems. In this context we consider tractability of such problems via a useful notion of semi-tractability and the introduction of a tractability index for a particular numerical solution algorithm. It is shown that in the discrete time case the WSM algorithm leads to semi-tractability of the corresponding optimal stopping problem in the sense that its complexity is bounded in order by accuracy to the power four, times logarithm of the dimension of the underlying Markov chain. Furthermore we study the WSM approach in the context of continuous time optimal stopping problems and derive the corresponding complexity bounds. Although we can not prove semi-tractability in this case, our bounds turn out to be the tightest ones among the complexity bounds known in the literature. We illustrate our theoretical findings by a numerical example. (joint work with Denis Belomestny and Maxim Kaledin to appear in Math. Fin.) |
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| 26.05.2020 | Dr. Alexandra Suvorikova (WIAS Berlin) |
| Analysis of 3D chromatin structure using Bures-Wasserstein distance | |
| 02.06.2020 | Dr. Sebastian Riedel (WIAS Berlin) |
| Runge-Kutta methods for rough differential equations |
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last reviewed: May 25, 2020 by Christine Schneider