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2020 – today
- 2024
[i8]Daan Roordink, Sibylle Hess:
Scoring rule nets: beyond mean target prediction in multivariate regression. CoRR abs/2409.14456 (2024)- 2023
- [c8]Daan Roordink
, Sibylle Hess:
Scoring Rule Nets: Beyond Mean Target Prediction in Multivariate Regression. ECML/PKDD (2) 2023: 190-205 - [e1]Bruno Crémilleux, Sibylle Hess, Siegfried Nijssen:
Advances in Intelligent Data Analysis XXI - 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12-14, 2023, Proceedings. Lecture Notes in Computer Science 13876, Springer 2023, ISBN 978-3-031-30046-2 [contents] - 2022
- [c7]Sebastian Buschjäger, Sibylle Hess, Katharina Morik:
Shrub Ensembles for Online Classification. AAAI 2022: 6123-6131 - [p1]Sibylle Hess:
Matrix Factorization with Binary Constraints. Mach. Learn. under Resour. Constraints Vol. 1 (1) 2022: 227-247 - 2021
- [j3]Sibylle Hess, Gianvito Pio, Michiel E. Hochstenbach, Michelangelo Ceci:
BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent. Data Min. Knowl. Discov. 35(6): 2542-2576 (2021) - [i7]Sebastian Buschjäger, Sibylle Hess, Katharina Morik:
Shrub Ensembles for Online Classification. CoRR abs/2112.03723 (2021) - 2020
- [j2]Wouter Duivesteijn, Sibylle Hess, Xin Du:
How to cheat the page limit. WIREs Data Mining Knowl. Discov. 10(3) (2020) - [i6]Sibylle Hess, Wouter Duivesteijn, Decebal Constantin Mocanu:
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring. CoRR abs/2001.01987 (2020)
2010 – 2019
- 2019
- [c6]Sibylle Hess, Wouter Duivesteijn, Philipp Honysz, Katharina Morik:
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. AAAI 2019: 3788-3795 - [c5]Sibylle Hess, Wouter Duivesteijn:
k Is the Magic Number - Inferring the Number of Clusters Through Nonparametric Concentration Inequalities. ECML/PKDD (1) 2019: 257-273 - [i5]Sibylle Hess, Katharina Morik, Nico Piatkowski:
The PRIMPing Routine - Tiling through Proximal Alternating Linearized Minimization. CoRR abs/1906.09722 (2019) - [i4]Sibylle Hess, Katharina Morik:
C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. CoRR abs/1906.09907 (2019) - [i3]Sibylle Hess, Wouter Duivesteijn, Philipp Honysz, Katharina Morik:
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. CoRR abs/1907.00680 (2019) - [i2]Sibylle Hess, Nico Piatkowski, Katharina Morik:
The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. CoRR abs/1907.00697 (2019) - [i1]Sibylle Hess, Wouter Duivesteijn:
k is the Magic Number - Inferring the Number of Clusters Through Nonparametric Concentration Inequalities. CoRR abs/1907.02343 (2019) - 2018
- [b1]Sibylle Hess:
A mathematical theory of making hard decisions: model selection and robustness of matrix factorization with binary constraints. Dortmund University, Germany, 2018 - [c4]Erich Schubert, Sibylle Hess, Katharina Morik:
The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. LWDA 2018: 330-334 - [c3]Sibylle Hess, Nico Piatkowski, Katharina Morik:
The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. SDM 2018: 405-413 - 2017
- [j1]Sibylle Hess, Katharina Morik, Nico Piatkowski:
The PRIMPING routine - Tiling through proximal alternating linearized minimization. Data Min. Knowl. Discov. 31(4): 1090-1131 (2017) - [c2]Sibylle Hess, Katharina Morik:
C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. ECML/PKDD (1) 2017: 547-563 - 2014
- [c1]Sibylle Hess, Nico Piatkowski, Katharina Morik:
SHrimp: Descriptive Patterns in a Tree. LWA 2014: 181-192
Coauthor Index
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