High resolution coding of stochastic processes and small ball probabilities. - Steffen Dereich (2003)

The Coding Complexity of Lévy Processes. - Frank Aurzada; Steffen Dereich (Found. Comput. Math., 2009)

Infinite-Dimensional Quadrature and Approximation of Distributions. - Jakob Creutzig; Steffen Dereich; Thomas Müller-Gronbach; Klaus Ritter 001 (Found. Comput. Math., 2009)

High resolution quantization and entropy coding of jump processes. - Frank Aurzada; Steffen Dereich; Michael Scheutzow; Christian Vormoor (J. Complex., 2009)

Multilevel Monte Carlo Implementation for SDEs Driven by Truncated Stable Processes. - Steffen Dereich; Sangmeng Li (MCQMC, 2014)

Robust Analysis of Preferential Attachment Models with Fitness. - Steffen Dereich; Marcel Ortgiese (Comb. Probab. Comput., 2014)

Quadrature for Self-affine Distributions on Rd. - Steffen Dereich; Thomas Müller-Gronbach (Found. Comput. Math., 2015)

Cycle length distributions in random permutations with diverging cycle weights. - Steffen Dereich; Peter Mörters (Random Struct. Algorithms, 2015)

General multilevel adaptations for stochastic approximation algorithms of Robbins-Monro and Polyak-Ruppert type. - Steffen Dereich; Thomas Müller-Gronbach (Numerische Mathematik, 2019)

Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes. - Steffen Dereich; Sebastian Kassing (CoRR, 2021)

On minimal representations of shallow ReLU networks. - Steffen Dereich; Sebastian Kassing (Neural Networks, 2022)

On the existence of optimal shallow feedforward networks with ReLU activation. - Steffen Dereich; Sebastian Kassing (CoRR, 2023)

Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses. - Steffen Dereich; Arnulf Jentzen; Adrian Riekert (CoRR, 2024)

Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates. - Steffen Dereich; Robin Graeber; Arnulf Jentzen (CoRR, 2024)

Convergence rates for the Adam optimizer. - Steffen Dereich; Arnulf Jentzen (CoRR, 2024)

On the Existence of Minimizers in Shallow Residual ReLU Neural Network Optimization Landscapes. - Steffen Dereich; Arnulf Jentzen; Sebastian Kassing (SIAM J. Numer. Anal., 2024)

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems. - Steffen Dereich; Arnulf Jentzen; Adrian Riekert (CoRR, 2025)

Mathematical analysis of the gradients in deep learning. - Steffen Dereich; Thang Do; Arnulf Jentzen; Frederic Weber (CoRR, 2025)

In almost all shallow analytic neural network optimization landscapes, efficient minimizers have strongly convex neighborhoods. - Felix Benning; Steffen Dereich (CoRR, 2025)

Sharp higher order convergence rates for the Adam optimizer. - Steffen Dereich; Arnulf Jentzen; Adrian Riekert (CoRR, 2025)

SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures. - Julian Kranz; Davide Gallon; Steffen Dereich; Arnulf Jentzen (CoRR, 2025)