Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. - Enrique Fita Sanmartin; Sebastian Damrich; Fred A. Hamprecht (NeurIPS, 2019)

Multistar: Instance Segmentation Of Overlapping Objects With Star-Convex Polygons. - Florin C. Walter; Sebastian Damrich; Fred A. Hamprecht (ISBI, 2021)

On UMAP's True Loss Function. - Sebastian Damrich; Fred A. Hamprecht (NeurIPS, 2021)

Directed Probabilistic Watershed. - Enrique Fita Sanmartin; Sebastian Damrich; Fred A. Hamprecht (NeurIPS, 2021)

UMAP does not reproduce high-dimensional similarities due to negative sampling. - Sebastian Damrich; Fred A. Hamprecht (CoRR, 2021)

The Algebraic Path Problem for Graph Metrics. - Enrique Fita Sanmartín; Sebastian Damrich; Fred A. Hamprecht (ICML, 2022)

Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. - Quentin Garrido; Sebastian Damrich; Alexander Jäger; Dario Cerletti; Manfred Claassen; Laurent Najman; Fred A. Hamprecht (Bioinform., 2022)

Contrastive learning unifies t-SNE and UMAP. - Sebastian Damrich; Jan Niklas Böhm; Fred A. Hamprecht; Dmitry Kobak (CoRR, 2022)

From $t$-SNE to UMAP with contrastive learning. - Sebastian Damrich; Jan Niklas Böhm; Fred A. Hamprecht; Dmitry Kobak (ICLR, 2023)

Geometric Autoencoders - What You See is What You Decode. - Philipp Nazari; Sebastian Damrich; Fred A. Hamprecht (ICML, 2023)

Discovering structure without labels. - Sebastian Damrich (2023)

Persistent Homology for High-dimensional Data Based on Spectral Methods. - Sebastian Damrich; Philipp Berens; Dmitry Kobak (NeurIPS, 2024)

Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122). - Dmitry Kobak; Fred A. Hamprecht; Smita Krishnaswamy; Gal Mishne; Sebastian Damrich (Dagstuhl Reports, 2024)

On the Importance of Embedding Norms in Self-Supervised Learning. - Andrew Draganov; Sharvaree P. Vadgama; Sebastian Damrich; Jan Niklas Böhm; Lucas Maes; Dmitry Kobak; Erik J. Bekkers (CoRR, 2025)

Node Embeddings via Neighbor Embeddings. - Jan Niklas Böhm; Marius Keute; Alica Guzmán; Sebastian Damrich; Andrew Draganov; Dmitry Kobak (CoRR, 2025)