News



       08.10.2020
Keynote talk at ICPM 2020
Data Mining on Process Data
T. Seidl


       24.09.2020
Accepted paper at ICDM 2020 Workshop HDM'20
I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering
D. Kazempour, A. Beer, P. Kröger, T. Seidl


       24.09.2020
Accepted paper at ICDM 2020 Workshop HDM'20
You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters
D. Kazempour, L. M. Yan, P. Kröger, T. Seidl


       24.09.2020
Accepted paper at ICDM 2020 Workshop HDM'20
Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering
D. Kazempour, P. Kröger, T. Seidl


       24.09.2020
Accepted paper at ICPM 2020 Workshop SA4PM 2020
Performance Skyline: Inferring Process Performance Models from Interval Events
A. Maldonado, J. Sontheim, F. Richter, T. Seidl


       11.09.2020
Moritz Herrmann receives the Bernd-Streitberg Award of the German Biometric Society
for his master thesis 'Large-scale benchmark study of prediction methods using multi-omics data' supervised by our PI Prof. Dr. Anne-Laure Boulesteix. In the context of MCML an article has been published in the renowned Journal 'Briefings in Bioinformatics' as an extension of his master thesis: Open Access


       13.08.2020
Accepted paper at SISAP 2020
Angle-Based Clustering
A. Beer, D. Seeholzer, N. Schüler, T. Seidl


       30.07.2020
Accepted paper at LWDA 2020
Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data
A. Beer, D. Kazempour, J. Busch, A. Tekles, T. Seidl


       29.07.2020
Virtual workshop
on 29.07.2020 with over 20 presentations by our PhD students on current research topics.


       02.07.2020
Announcement PyKEEN 1.0
As a member of the PyKEEN community project, we are happy to announce PyKEEN 1.0 – PyKEEN is a software package to train and evaluate knowledge graph embedding models.


       01.06.2020
18 papers accepted at CVPR
The PIs of TU Munich published 18 papers at CVF/IEEE Conference on Computer Vision and Pattern Recognition (CVPR) this year, the conference with the currently highest impact among all scientific conferences worldwide.


       28.05.2020
Accepted short vision paper at SSDBM 2020
Orderings of Data - more than a Tripping Hazard
A. Beer, V. Hartmann, T. Seidl


       06.02.2020
Accepted paper at PAKDD 2020
Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform
F. Borutta, D. Kazempour, F. Marty, P. Kröger, T. Seidl


       20.12.2019
MCML Upgrade
We are happy to announce that the MCML upgrade proposal is now accepted by BMBF.


       19.12.2019
Accepted paper at ECIR 2020 reproducibility track
Knowledge Graph Entity Alignment with Graph Neural Networks: Lessons Learned
M. Berrendorf, E. Faehrman, V. Melnychuk, V. Tresp, T. Seidl


       14.11.2019
3rd Place at ACM SIGSPATIAL GisCup 2019
goes to F. Borutta, S. Schmoll, S. Friedl for their contribution 'Optimizing the Spatio-Temporal Resource Search Problem with Reinforcement Learning'


       10.10.2019
Accepted paper at NeurIPS Workshop 2019
Graph Alignment Networks with Node Matching Scores
E. Faerman, O. Voggenreiter, F. Borutta, T. Emrich, M. Berrendorf, M. Schubert


       12.09.2019
Accepted paper at ICDM Workshop DeepSpatial 2019
Spatial Interpolation with Message Passing Framework
E. Faerman, M. Rogalla, N. Strauß, A. Krüger, B. Blümel, M. Berrendorf, M. Fromm, M.Schubert


       12.09.2019
Accepted journal paper at Datenbank-Spektrum 19 (2019)
Chain-detection Between Clusters
J. Held, A. Beer, T. Seidl


       22.08.2019
Accepted paper at ACM SIGSPATIAL 2019
Optimizing the Spatio-Temporal Resource Search Problem with Reinforcement Learning
F. Borutta, S. Schmoll, S. Friedl


       25.07.2019
Accepted paper at LWDA 2019
A Generator for Subspace Clusters
A. Beer, N. S. Schüler, T. Seidl


       25.07.2019
Accepted paper at WI 2019
Structural Graph Representations based on Multiscale Local Network Topologies
F. Borutta, J. Busch, E. Faerman, A. Klink, M. Schubert


       25.07.2019
Accepted paper at LWDA 2019
Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents
D. Kazempour, A. Beer, O. Schrüfer, T. Seidl


       25.07.2019
Accepted paper at LWDA 2019
CODEC - Detecting Linear Correlations in Dense Clusters with Comedian-based PCA
D. Kazempour, M. Hunemörder, A. Beer, and T. Seidl


       25.07.2019
Accepted paper at LWDA 2019
From Covariance to Comode in context of Principal Component Analysis
D. Kazempour, L. M. Yan, T. Seidl


       11.07.2019
Accepted paper at SISAP 2019
MORe++: k-Means Based Outlier Removal on High-Dimensional Data
A. Beer, J. Lauterbach, T. Seidl


       11.07.2019
Accepted paper at SISAP 2019
k-Distance Approximation for Memory-Efficient RkNN Retrieval
M. Berrendorf, F. Borutta, P. Kröger


       11.07.2019
Accepted paper at SISAP 2019
A Generic Summary Structure for Arbitrarily Oriented Subspace Clustering in Data Streams
F. Borutta, P. Kröger, T. Hubauer


       11.07.2019
Accepted paper at SISAP 2019
On coMADs and Principal Component Analysis
D. Kazempour, M. Hünemörder, T. Seidl


       11.07.2019
Accepted paper at SISAP 2019
On Co-medians and Principal Component Analysis
D. Kazempour, T. Seidl


       16.05.2019
Accepted short paper at SSDBM 2019
Graph Ordering and Clustering - A Circular Approach
A. Beer, T. Seidl


       16.05.2019
Accepted short paper at SSDBM 2019
LUCK - Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function
A. Beer, D. Kazempour, L. Stephan, T. Seidl


       16.05.2019
On systematic hyperparameter analysis through the example of subspace clustering
Graph Ordering and Clustering - A Circular Approach
D. Kazempour, T. Seidl


       16.05.2019
Accepted short paper at SSDBM 2019
Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform
D. Kazempour, K. Emmerig, P. Kröger, and T. Seidl


       29.03.2019
Accepted paper at ICDM Workshop DeepSpatial 2019
XD-STOD: Cross-Domain Superresolution for Tiny Object Detection
M. Fromm, M. Berrendorf, E. Faerman, Y. Chen, B. Schüss, M. Schubert


       12.03.2019
Accepted poster at HCII 2019
Human Learning in Data Science
A. Beer, D. Kazempour, M. Baur, T. Seidl


       12.03.2019
Accepted poster at HCII 2019
Data on RAILs: On interactive generation of artificial linear correlated data
D. Kazempour, A. Beer, and T. Seidl


       27.01.2019
LMU Munich Magazine 'Einsichten'
Smarte Wesen: Hat das mit Intelligenz zu tun? Der Informatiker Thomas Seidl und der Statistiker Bernd Bischl debattieren über Maschinelles Lernen, andere Formen von KI und darüber, warum es wichtig ist zu verstehen, wie Rechner damit zu ihren Ergebnissen kommen.


       17.01.2019
Press release LMU Munich
Neues KI-Projekt geht an den Start


       17.01.2019
Press release LMU Munich
Munich Center for Machine Learning geht an den Start


       15.01.2019
Accepted demo paper at BTW 2019
DICE: Density-based Interactive Clustering and Exploration
D. Kazempour, M. Kazakov, P. Kröger, T. Seidl.


       08.01.2019
Accepted short paper at EDBT 2019
Rock - Let the points roam to their clusters themselvese
A. Beer, D. Kazempour, T. Seidl


       08.01.2019
Accepted short paper at EDBT 2019
A Galaxy of Correlations - Detecting Linear Correlated Clusters through k-Tuples Sampling using Parameter Space Transform
D. Kazempour, L. Krombholz, P. Kröger, T. Seidl


       08.01.2019
Accepted short paper at EDBT 2019
Insights into a running clockwork: On interactive process-aware clustering
D. Kazempour, T. Seidl


       11.12.2018
Accepted poster at BTW 2019
Chain-detection for DBSCAN
J. Held, A. Beer, T. Seidl