Framework for Exploring and Understanding Multivariate Correlations. - Louis Kirsch; Niklas Riekenbrauck; Daniel Thevessen; Marcus Pappik; Axel Stebner; Julius Kunze; Alexander Meissner; Arvind Kumar Shekar; Emmanuel Müller (ECML/PKDD (3), 2017)

Selection of Relevant and Non-Redundant Multivariate Ordinal Patterns for Time Series Classification. - Arvind Kumar Shekar; Marcus Pappik; Patricia Iglesias Sánchez; Emmanuel Müller (DS, 2018)

Convergence and Hardness of Strategic Schelling Segregation. - Hagen Echzell; Tobias Friedrich 001; Pascal Lenzner; Louise Molitor; Marcus Pappik; Friedrich Schöne; Fabian Sommer; David Stangl (WINE, 2019)

Polymer dynamics via cliques with applications to hard-sphere mixtures. - Tobias Friedrich 001; Andreas Göbel 001; Martin S. Krejca; Marcus Pappik (CoRR, 2020)

Algorithms for general hard-constraint point processes via discretization. - Tobias Friedrich 001; Andreas Göbel 001; Maximilian Katzmann; Martin S. Krejca; Marcus Pappik (CoRR, 2021)

Algorithms for Hard-Constraint Point Processes via Discretization. - Tobias Friedrich 001; Andreas Göbel 001; Maximilian Katzmann; Martin S. Krejca; Marcus Pappik (COCOON, 2022)

Analysis of a gray-box operator for vertex cover. - Samuel Baguley; Tobias Friedrich 001; Timo Kötzing; Xiaoyue Li 001; Marcus Pappik; Ziena Zeif (GECCO, 2022)

Using random graphs to sample repulsive Gibbs point processes with arbitrary-range potentials. - Tobias Friedrich 001; Andreas Göbel 001; Maximilian Katzmann; Martin S. Krejca; Marcus Pappik (CoRR, 2022)

A Spectral Independence View on Hard Spheres via Block Dynamics. - Tobias Friedrich 001; Andreas Göbel 001; Martin S. Krejca; Marcus Pappik (SIAM J. Discret. Math., 2022)

Counting and Sampling: Algorithms and Complexity (Dagstuhl Seminar 22482). - Holger Dell; Mark Richard Jerrum; Haiko Müller; Konrad Anand; Marcus Pappik (Dagstuhl Reports, 2022)

Perfect Sampling for Hard Spheres from Strong Spatial Mixing. - Konrad Anand; Andreas Göbel 001; Marcus Pappik; Will Perkins 001 (APPROX/RANDOM, 2023)

Polymer dynamics via cliques: New conditions for approximations. - Tobias Friedrich 001; Andreas Göbel 001; Martin S. Krejca; Marcus Pappik (Theor. Comput. Sci., 2023)

The Irrelevance of Influencers: Information Diffusion with Re-Activation and Immunity Lasts Exponentially Long on Social Network Models. - Tobias Friedrich 001; Andreas Göbel 001; Nicolas Klodt; Martin S. Krejca; Marcus Pappik (AAAI, 2024)

Fast and Slow Mixing of the Kawasaki Dynamics on Bounded-Degree Graphs. - Aiya Kuchukova; Marcus Pappik; Will Perkins 001; Corrine Yap (APPROX/RANDOM, 2024)

From Market Saturation to Social Reinforcement: Understanding the Impact of Non-Linearity in Information Diffusion Models. - Tobias Friedrich 001; Andreas Göbel 001; Nicolas Klodt; Martin S. Krejca; Marcus Pappik (AAMAS, 2024)

Lazy brute-force sampling: A universal perfect sampling scheme from Markov chains. - Andreas Göbel 001; Marcus Pappik (CoRR, 2024)

Testing Thresholds and Spectral Properties of High-Dimensional Random Toroidal Graphs via Edgeworth-Style Expansions. - Samuel Baguley; Andreas Göbel 001; Marcus Pappik; Leon Schiller (COLT, 2025)

Sampling repulsive Gibbs point processes using random graphs. - Tobias Friedrich 001; Andreas Göbel 001; Maximilian Katzmann; Martin S. Krejca; Marcus Pappik (Comb. Probab. Comput., 2025)

Fixed Parameter Multi-Objective Evolutionary Algorithms for the W-Separator Problem. - Samuel Baguley; Tobias Friedrich 001; Aneta Neumann; Frank Neumann 001; Marcus Pappik; Ziena Zeif (Algorithmica, 2025)