Scalable Graph Networks for Particle Simulations. - Karolis Martinkus; Aurélien Lucchi; Nathanaël Perraudin (AAAI, 2021)

DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks. - Pál András Papp; Karolis Martinkus; Lukas Faber; Roger Wattenhofer (NeurIPS, 2021)

On Isotropy Calibration of Transformers. - Yue Ding 003; Karolis Martinkus; Damian Pascual; Simon Clematide; Roger Wattenhofer (CoRR, 2021)

On Isotropy Calibration of Transformer Models. - Yue Ding 003; Karolis Martinkus; Damian Pascual; Simon Clematide; Roger Wattenhofer (Insights@ACL, 2022)

SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators. - Karolis Martinkus; Andreas Loukas; Nathanaël Perraudin; Roger Wattenhofer (ICML, 2022)

DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees. - Peter Müller; Lukas Faber; Karolis Martinkus; Roger Wattenhofer (CoRR, 2022)

Diffusion Models for Graphs Benefit From Discrete State Spaces. - Kilian Konstantin Haefeli; Karolis Martinkus; Nathanaël Perraudin; Roger Wattenhofer (CoRR, 2022)

Agent-based Graph Neural Networks. - Karolis Martinkus; Pál András Papp; Benedikt Schesch; Roger Wattenhofer (ICLR, 2023)

Automating Rigid Origami Design. - Jeremia Geiger; Karolis Martinkus; Oliver Richter; Roger Wattenhofer (IJCAI, 2023)

AbDiffuser: full-atom generation of in-vitro functioning antibodies. - Karolis Martinkus; Jan Ludwiczak; Wei-Ching Liang; Julien Lafrance-Vanasse; Isidro Hötzel; Arvind Rajpal; Yan Wu 027; Kyunghyun Cho; Richard Bonneau; Vladimir Gligorijevic; Andreas Loukas (NeurIPS, 2023)

Discovering Graph Generation Algorithms. - Mihai Babiac; Karolis Martinkus; Roger Wattenhofer (CoRR, 2023)

Learning to Understand Graph Structure: From Classification to Generation. - Karolis Martinkus (2023)

Efficient and Scalable Graph Generation through Iterative Local Expansion. - Andreas Bergmeister; Karolis Martinkus; Nathanaël Perraudin; Roger Wattenhofer (ICLR, 2024)

GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks. - Peter Müller; Lukas Faber; Karolis Martinkus; Roger Wattenhofer (ICLR, 2024)

Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing. - Andreas Loukas; Karolis Martinkus; Ed Wagstaff; Kyunghyun Cho (CoRR, 2024)

Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins. - Frédéric A. Dreyer; Jan Ludwiczak; Karolis Martinkus; Brennan Abanades; Robert G. Alberstein; Pan Kessel; Pranav Rao; Jae Hyeon Lee; Richard Bonneau; Andrew M. Watkins; Franziska Seeger (CoRR, 2025)