Monotonicity, Duplication Monotonicity, and Pareto Optimality in the Scoring-Based Allocation of Indivisible Goods. - Benno Kuckuck; Jörg Rothe (AT, 2018)

Sequential Allocation Rules are Separable: Refuting a Conjecture on Scoring-Based Allocation of Indivisible Goods. - Benno Kuckuck; Jörg Rothe (AAMAS, 2018)

Refugee Allocation in the Setting of Hedonic Games. - Benno Kuckuck; Jörg Rothe; Anke Weißenfeld (ADT, 2019)

Duplication monotonicity in the allocation of indivisible goods. - Benno Kuckuck; Jörg Rothe (AI Commun., 2019)

Full error analysis for the training of deep neural networks. - Christian Beck; Arnulf Jentzen; Benno Kuckuck (CoRR, 2019)

An overview on deep learning-based approximation methods for partial differential equations. - Christian Beck; Martin Hutzenthaler; Arnulf Jentzen; Benno Kuckuck (CoRR, 2020)

Strong Lp-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations. - Martin Hutzenthaler; Arnulf Jentzen; Benno Kuckuck; Joshua Lee Padgett (CoRR, 2021)

Deep neural network approximation theory for high-dimensional functions. - Pierfrancesco Beneventano; Patrick Cheridito; Robin Graeber; Arnulf Jentzen; Benno Kuckuck (CoRR, 2021)

Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions. - Victor Boussange; Sebastian Becker; Arnulf Jentzen; Benno Kuckuck; Loïc Pellissier (CoRR, 2022)

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the Lp-sense. - Julia Ackermann; Arnulf Jentzen; Thomas Kruse; Benno Kuckuck; Joshua Lee Padgett (CoRR, 2023)

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory. - Arnulf Jentzen; Benno Kuckuck; Philippe von Wurstemberger (CoRR, 2023)

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for space-time solutions of semilinear partial differential equations. - Julia Ackermann; Arnulf Jentzen; Benno Kuckuck; Joshua Lee Padgett (CoRR, 2024)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning. - Lukas Gonon; Arnulf Jentzen; Benno Kuckuck; Siyu Liang 001; Adrian Riekert; Philippe von Wurstemberger (CoRR, 2024)

On the logical skills of large language models: evaluations using arbitrarily complex first-order logic problems. - Shokhrukh Ibragimov; Arnulf Jentzen; Benno Kuckuck (CoRR, 2025)