Archive: Stochastik Rhein-Main

16.11.2017 Oberseminar Darmstadt

Kay Hamacher (TU Darmstadt / FB Biologie)

Permutation Tests, Resampling and the like in Biostatistics

08.07.2025 Oberseminar Mainz

Réka Szabó (University of Groningen)

Percolation on the stationary distributions of the voter model with stirring

30.11.2017 Oberseminar Darmstadt

David Dereudre (Université Lille 1)

Path-dependent infinite-dimensional SDE with non-regular drift: an existence result

Karl-Theodor Sturm (Universität Bonn)

Optimal transport, heat flow, and Ricci curvature on metric measure spaces

07.07.2020 Oberseminar Mainz

Matthias Birkner (Johannes Gutenberg-Universität Mainz)

Onsagers Lösung des zweidimensionalen Ising-Modells

17.07.2025 Oberseminar Darmstadt

Ulrik Hansen (Universität Innsbruck)

One Theorem, 16 Couplings

19.11.2024 Oberseminar Mainz

Sascha Franck (Universität Frankfurt)

On the spread of an infection in a spatially distributed host population with host immunity

21.01.2025 Oberseminar Mainz

Mareike Fischer (Universität Greifswald)

On the reliability of Maximum Parsimony for encoding and reconstructing phylogenetic trees

03.07.2025 Oberseminar Darmstadt

Jochen Blath (Goethe-Universität Frankfurt)

On the evolutionary, ecological and pathogenic consequences of dormancy - recent progress and open challenges

07.02.2023 Oberseminar Mainz

Prof. Dr. Steffen Dereich (Universität Münster)

Oberseminar Stochastik Mainz - 07.05.2024 - Prof. Dr. Steffen Dereich (WWU Münster)

Titel: New error bounds for the Adam optimisation algorithm Abstract: In this talk, I will give an introduction concerning the training of artificial neural networks based on stochastic gradient descent algorithms. Whereas there are various approaches for carrying out an error analysis for classical stochastic gradient descent, significantly less is known for more elaborate algorithms such as the Adam algorithm. Recently, we established a new approach which allows us to provide error estimates and to reduce this gap. The talk is based on joint work with Arnulf Jentzen. Read more

11.05.2021 Oberseminar Mainz

Iulia Dahmer (Johannes-Gutenberg-Universität Mainz)

Oberseminar Stochastik

01.06.2021 Oberseminar Mainz

Dr. Christian Mönch (Johannes Gutenberg-Universität Mainz)

Oberseminar Stochastik

08.06.2021 Oberseminar Mainz

Andreas Meier (Johannes Gutenberg-Universität Mainz)

Oberseminar Stochastik

25.05.2021 Oberseminar Mainz

Leonard Bauer (Johannes Gutenberg-Universität Mainz)

Oberseminar Stochastik

13.01.2026 Oberseminar Mainz

Janine Piesold (Johannes Gutenberg-Universität Mainz)

Oberseminar Mainz 13.01.2026

15.04.2025 Oberseminar Mainz

Réka Szabó (University of Groningen)

Oberseminar Mainz

How single cell properties can change network synchronization: The saddle-node loop bifurcation in neuron models - Neurons have traditionally been classified into two types depending on their frequency-input curve. Both types are associated with a particular dynamic transition from rest to spiking. Our work highlights a third transition, for which we found experimental evidence in hippocampal slices. For typical Hodgkin-Huxley-like neuron model, we present a universal bifurcation structure, with the separation of timescale between voltage and ion channel dynamics as one of the bifurcation parameters. We predict that the strongest changes in synchronization with small parameter changes occur at a particular co-dimension two bifurcation, the saddle-node loop bifurcation, and we present characteristics of this transition, from changes in firing rate to phase response curve and synchronization. We will conclude with a short overview over our current research on stress resilience. Read more

08.11.2017 Stochastik-Kolloquium Frankfurt

Prof. Dr. Sebastian Doehler (Hochschule Darmstadt)

New false discovery rate controlling procedures for discrete data

07.05.2024 Oberseminar Mainz

Prof. Dr. Steffen Dereich (Universität Münster)

New error bounds for the Adam optimisation algorithm

In this talk, I will give an introduction concerning the training of artifcial neural networks based on stochastic gradient descent algorithms. Whereas there are various approaches for carrying out an error analysis for classical stochastic gradient descent, significantly less is known for more elaborate algorithms such as the Adam algorithm. Recently, we established a new approach which allows us to provide error estimates and to reduce this gap. The talk is based on joint work with Arnulf Jentzen. Read more

17.11.2016 Oberseminar Darmstadt

Benjamin Lees (University of Leeds)

Néel order using a random loop model

03.12.2024 Oberseminar Mainz

Sebastian Hummel (ETH Zürich)

Multi-Type Birth-Death Processes with Mean-Field Interactions for B-cell Phylodynamics

Events archive

Events

Permutation Tests, Resampling and the like in Biostatistics

Percolation on the stationary distributions of the voter model with stirring

Path-dependent infinite-dimensional SDE with non-regular drift: an existence result

Optimal transport, heat flow, and Ricci curvature on metric measure spaces

Onsagers Lösung des zweidimensionalen Ising-Modells

One Theorem, 16 Couplings

On the spread of an infection in a spatially distributed host population with host immunity

On the reliability of Maximum Parsimony for encoding and reconstructing phylogenetic trees

On the evolutionary, ecological and pathogenic consequences of dormancy - recent progress and open challenges

Oberseminar Stochastik Mainz - 07.05.2024 - Prof. Dr. Steffen Dereich (WWU Münster)

Oberseminar Stochastik

Oberseminar Stochastik

Oberseminar Stochastik

Oberseminar Stochastik

Oberseminar Mainz 13.01.2026

Oberseminar Mainz

New false discovery rate controlling procedures for discrete data

New error bounds for the Adam optimisation algorithm

Néel order using a random loop model

Multi-Type Birth-Death Processes with Mean-Field Interactions for B-cell Phylodynamics

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