SHrimp: Descriptive Patterns in a Tree. - Sibylle Hess; Nico Piatkowski; Katharina Morik (LWA, 2014)

C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. - Sibylle Hess; Katharina Morik (ECML/PKDD (1), 2017)

The PRIMPING routine - Tiling through proximal alternating linearized minimization. - Sibylle Hess; Katharina Morik; Nico Piatkowski (Data Min. Knowl. Discov., 2017)

The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. - Erich Schubert; Sibylle Hess; Katharina Morik (LWDA, 2018)

The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. - Sibylle Hess; Nico Piatkowski; Katharina Morik (SDM, 2018)

A mathematical theory of making hard decisions: model selection and robustness of matrix factorization with binary constraints - Sibylle Hess (2018)

The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. - Sibylle Hess; Wouter Duivesteijn; Philipp Honysz; Katharina Morik (AAAI, 2019)

k Is the Magic Number - Inferring the Number of Clusters Through Nonparametric Concentration Inequalities. - Sibylle Hess; Wouter Duivesteijn (ECML/PKDD (1), 2019)

Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring. - Sibylle Hess; Wouter Duivesteijn; Decebal Constantin Mocanu (CoRR, 2020)

How to cheat the page limit. - Wouter Duivesteijn; Sibylle Hess; Xin Du 006 (WIREs Data Mining Knowl. Discov., 2020)

BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent. - Sibylle Hess; Gianvito Pio; Michiel E. Hochstenbach; Michelangelo Ceci (Data Min. Knowl. Discov., 2021)

Matrix Factorization with Binary Constraints. - Sibylle Hess (Mach. Learn. under Resour. Constraints Vol. 1 (1), 2022)

Shrub Ensembles for Online Classification. - Sebastian Buschjäger; Sibylle Hess; Katharina Morik (AAAI, 2022)

Scoring Rule Nets: Beyond Mean Target Prediction in Multivariate Regression. - Daan Roordink; Sibylle Hess (ECML/PKDD (2), 2023)

Purifying Adversarial Examples Using an Autoencoder. - Thijs van Weezel; Famke van Ree; Tychon Bos; Patrick Bastiaanssen; Sibylle Hess (DS (2), 2024)

Occam's model: Selecting simpler representations for better transferability estimation. - Prabhant Singh; Sibylle Hess; Joaquin Vanschoren (CoRR, 2025)

A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology. - Hassan Keshvarikhojasteh; Mihail Tifrea; Sibylle Hess; Josien P. W. Pluim; Mitko Veta (CoRR, 2025)