Rieck Lab

2021

Brüningk S.C. ; Hensel F. ; Lukas L. ; Kuijs M. ; Jutzeler C.R. ; Rieck B.
Back to the basics with inclusion of clinical domain knowledge — A simple, scalable, and effective model of Alzheimer’s Disease classification.
Proceedings of Machine Learning Research 149: 1-24 (2021)

O’Bray L. ; Rieck B. ; Borgwardt K.
Filtration Curves for Graph Representation Fil­tra­tion Curves for Graph Rep­re­sen­ta­tion
Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), DOI: 10.1145/3447548.3467442 (2021)

Moor M. ; Rieck B. ; Horn M. ; Jutzeler C.R. ; Borgwardt K.
Early Pre­dic­tion of Sep­sis in the ICU Us­ing Ma­chine Learn­ing: A Sys­tem­atic Re­view
Frontiers in Medicine 8: 607952, DOI: 10.3389/fmed.2021.607952 (2021)

Hensel F. ; Moor M. ; Rieck B.
A Sur­vey of Topo­log­i­cal Ma­chine Learn­ing Meth­ods
Frontiers in Artificial Intelligence 4: 681108, DOI: 10.3389/frai.2021.681108 (2021)

Vandaele R. ; Rieck B. ; Saeys Y. ; De Bie T.
Sta­ble Topo­log­i­cal Sig­na­tures for Met­ric Trees through Graph Ap­prox­i­ma­tions
Pattern Recognition Letters 147, pp. 85–92, DOI: 10.1016/j.patrec.2021.03.035 (2021)

2020

Rieck B. ; Yates T. ; Bock C. ; Borgwardt K. ; Wolf G. ; Turk-Browne N. ; Krishnaswamy S.
Un­cov­er­ing the Topol­ogy of Time-Vary­ing fMRI Data us­ing Cu­bi­cal Per­sis­tence
Advances in Neural Information Processing Systems (NeurIPS), Volume 33, pp. 6900–6912, arXiv: 2006.07882 (2020)

Borgwardt K. ; Ghisu E. ; Llinares-López F. ; O’Bray L. ; Rieck B.
Graph Ker­nels: State-of-the-Art and Fu­ture Chal­lenges
Foundations and Trends® in Machine Learning 13:5–6, pp. 531–712, DOI: 10.1561/2200000076 (2020)