Learning MSO-definable hypotheses on strings. - Martin Grohe; Christof Löding; Martin Ritzert (ALT, 2017)

Learning first-order definable concepts over structures of small degree. - Martin Grohe; Martin Ritzert (LICS, 2017)

Learning MSO-definable hypotheses on string. - Martin Grohe; Christof Löding; Martin Ritzert (CoRR, 2017)

Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks. - Christopher Morris 001; Martin Ritzert; Matthias Fey; William L. Hamilton; Jan Eric Lenssen; Gaurav Rattan; Martin Grohe (AAAI, 2019)

Learning Definable Hypotheses on Trees. - Émilie Grienenberger; Martin Ritzert (ICDT, 2019)

RUN-CSP: Unsupervised Learning of Message Passing Networks for Binary Constraint Satisfaction Problems. - Jan Tönshoff; Martin Ritzert; Hinrikus Wolf; Martin Grohe (CoRR, 2019)

The Effects of Randomness on the Stability of Node Embeddings. - Tobias Schumacher 002; Hinrikus Wolf; Martin Ritzert; Florian Lemmerich; Jan Bachmann; Florian Frantzen; Max Klabunde; Martin Grohe; Markus Strohmaier (CoRR, 2020)

Graph Neural Networks for Maximum Constraint Satisfaction. - Jan Tönshoff; Martin Ritzert; Hinrikus Wolf; Martin Grohe (Frontiers Artif. Intell., 2020)

The Effects of Randomness on the Stability of Node Embeddings. - Tobias Schumacher 002; Hinrikus Wolf; Martin Ritzert; Florian Lemmerich; Martin Grohe; Markus Strohmaier (PKDD/ECML Workshops (1), 2021)

Graph Learning with 1D Convolutions on Random Walks. - Jan Tönshoff; Martin Ritzert; Hinrikus Wolf; Martin Grohe (CoRR, 2021)

On the Parameterized Complexity of Learning Logic. - Steffen van Bergerem; Martin Grohe; Martin Ritzert (CoRR, 2021)

Learning on graphs with logic and neural networks. - Martin Ritzert (2021)

Optimal Weak to Strong Learning. - Kasper Green Larsen; Martin Ritzert (NeurIPS, 2022)

On the Parameterized Complexity of Learning First-Order Logic. - Steffen van Bergerem; Martin Grohe; Martin Ritzert (PODS, 2022)

Graph Machine Learning for Design of High-Octane Fuels. - Jan G. Rittig; Martin Ritzert; Artur M. Schweidtmann; Stefanie Winkler; Jana M. Weber; Philipp Morsch; K. Alexander Heufer; Martin Grohe; Alexander Mitsos; Manuel Dahmen (CoRR, 2022)

AdaBoost is not an Optimal Weak to Strong Learner. - Mikael Møller Høgsgaard; Kasper Green Larsen; Martin Ritzert (ICML, 2023)

Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing. - Jan Tönshoff; Martin Ritzert; Hinrikus Wolf; Martin Grohe (Trans. Mach. Learn. Res., 2023)

Distinguished In Uniform: Self-Attention Vs. Virtual Nodes. - Eran Rosenbluth; Jan Tönshoff; Martin Ritzert; Berke Kisin; Martin Grohe (ICLR, 2024)

MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions. - Polina Turishcheva; Laura Hansel; Martin Ritzert; Marissa A. Weis; Alexander S. Ecker (CoRR, 2024)

Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark. - Jan Tönshoff; Martin Ritzert; Eran Rosenbluth; Martin Grohe (Trans. Mach. Learn. Res., 2024)

Boosting, Voting Classifiers and Randomized Sample Compression Schemes. - Arthur da Cunha 001; Kasper Green Larsen; Martin Ritzert (ALT, 2025)

DISCO: Internal Evaluation of Density-Based Clustering. - Anna Beer 001; Lena Krieger 001; Pascal Weber 001; Martin Ritzert; Ira Assent; Claudia Plant (CoRR, 2025)

Hierarchical clustering with maximum density paths and mixture models. - Martin Ritzert; Polina Turishcheva; Laura Hansel; Paul Wollenhaupt; Marissa A. Weis; Alexander S. Ecker (CoRR, 2025)