Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. - Moritz Seiler; Heike Trautmann; Pascal Kerschke (IJCNN, 2020)

FakeYou! - A Gamified Approach for Building and Evaluating Resilience Against Fake News. - Lena Clever; Dennis Assenmacher; Kilian Müller; Moritz Vinzent Seiler; Dennis M. Riehle; Mike Preuss; Christian Grimme (MISDOOM, 2020)

Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. - Moritz Seiler; Janina Pohl; Jakob Bossek; Pascal Kerschke; Heike Trautmann (PPSN (1), 2020)

On the potential of normalized TSP features for automated algorithm selection. - Jonathan Heins; Jakob Bossek; Janina Pohl; Moritz Seiler; Heike Trautmann; Pascal Kerschke (FOGA, 2021)

RP-Mod&RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. - Dennis Assenmacher; Marco Niemann; Kilian Müller; Moritz Seiler; Dennis M. Riehle; Heike Trautmann (NeurIPS Datasets and Benchmarks, 2021)

Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. - Raphael Patrick Prager; Moritz Vinzent Seiler; Heike Trautmann; Pascal Kerschke (SSCI, 2021)

A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes. - Moritz Vinzent Seiler; Raphael Patrick Prager; Pascal Kerschke; Heike Trautmann (GECCO, 2022)

Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. - Janina Susanne Pohl; Dennis Assenmacher; Moritz Vinzent Seiler; Heike Trautmann; Christian Grimme (ICWSM Workshops, 2022)

Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. - Raphael Patrick Prager; Moritz Vinzent Seiler; Heike Trautmann; Pascal Kerschke (PPSN (1), 2022)

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. - Moritz Vinzent Seiler; Jeroen Rook; Jonathan Heins; Oliver Ludger Preuß; Jakob Bossek; Heike Trautmann (SSCI, 2023)

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. - Fabian Eitel; Marc-André Schulz; Moritz Seiler; Henrik Walter; Kerstin Ritter (CoRR, 2023)

A study on the effects of normalized TSP features for automated algorithm selection. - Jonathan Heins; Jakob Bossek; Janina Pohl; Moritz Seiler; Heike Trautmann; Pascal Kerschke (Theor. Comput. Sci., 2023)

Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection. - Moritz Seiler; Urban Skvorc; Carola Doerr; Heike Trautmann (LION, 2024)

Learned Features vs. Classical ELA on Affine BBOB Functions. - Moritz Seiler; Urban Skvorc; Gjorgjina Cenikj; Carola Doerr; Heike Trautmann (PPSN (2), 2024)

Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems. - Moritz Vinzent Seiler; Pascal Kerschke; Heike Trautmann (CoRR, 2024)

Deep reinforcement learning for instance-specific algorithm configuration. - Elias Schede; Moritz Seiler; Kevin Tierney; Heike Trautmann (GECCO, 2025)

RandOptGen: A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces. - Moritz Vinzent Seiler; Oliver Ludger Preuß; Heike Trautmann (GECCO, 2025)

Landscape features in single-objective continuous optimization: Have we hit a wall in algorithm selection generalization? - Gjorgjina Cenikj; Gasper Petelin; Moritz Seiler; Nikola Cenikj; Tome Eftimov (Swarm Evol. Comput., 2025)