Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies. - Robin Vandaele; Tijl De Bie; Yvan Saeys (ECML/PKDD (2), 2018)

Graph Approximations to Geodesics on Metric Graphs. - Robin Vandaele; Yvan Saeys; Tijl De Bie (ICPR, 2020)

Mining Topological Structure in Graphs through Forest Representations. - Robin Vandaele; Yvan Saeys; Tijl De Bie (J. Mach. Learn. Res., 2020)

The Curse Revisited: a Newly Quantified Concept of Meaningful Distances for Learning from High-Dimensional Noisy Data. - Robin Vandaele; Bo Kang; Tijl De Bie; Yvan Saeys (CoRR, 2021)

Stable topological signatures for metric trees through graph approximations. - Robin Vandaele; Bastian Rieck; Yvan Saeys; Tijl De Bie (Pattern Recognit. Lett., 2021)

The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data? - Robin Vandaele; Bo Kang; Tijl De Bie; Yvan Saeys (AISTATS, 2022)

Topologically Regularized Data Embeddings. - Robin Vandaele; Bo Kang; Jefrey Lijffijt; Tijl De Bie; Yvan Saeys (ICLR, 2022)

Topologically Regularized Data Embeddings. - Edith Heiter; Robin Vandaele; Tijl De Bie; Yvan Saeys; Jefrey Lijffijt (CoRR, 2023)

Topological data analysis of thoracic radiographic images shows improved radiomics-based lung tumor histology prediction. - Robin Vandaele; Pritam Mukherjee; Heather Marie Selby; Rajesh Pravin Shah; Olivier Gevaert (Patterns, 2023)

Incorporating Topological Priors Into Low-Dimensional Visualizations Through Topological Regularization. - Edith Heiter; Robin Vandaele; Tijl De Bie; Yvan Saeys; Jefrey Lijffijt (IEEE Access, 2024)