News Archive at MCML
17.12.2020
Advertisement Feature in Nature Research
LMU Munich harnesses AI to drive discovery
'Give me your data and we’ll find patterns,' says Thomas Seidl, a computer scientist at LMU Munich. Decoding such patterns reveals secrets hidden in everything from bone fossils to ancient languages. And as academia and industry collect more and more data, there is growing interest in using artificial intelligence (AI) techniques to analyse them...
17.12.2020
Researching Artificial Intelligence at LMU
Advancements in artificial intelligence (AI) open up new perspectives for science - As a central hub for highly innovative research in Europe, LMU Munich plays a key role in probing both the potential of AI and confronting the challenges it presents...
03.12.2020
The Marie Curie Innovative Training Network entitled “Machine Learning Frontiers in Precision Medicine” brings together leading European research institutes in machine learning and statistical genetics, both from the private and public sector, to train 14 early stage researchers. These scientists will apply machine learning methods to health data. The goal is to reveal new insights into disease mechanisms and therapy outcomes and to exploit the findings for precision medicine, which hopes to offer personalized preventive care and therapy selection for each patient.
26.11.2020
Vacancy: PostDoc in the area of Machine Learning and Data Analytics (f/m/d)
We are looking for talented and highly motivated computer scientists (or people with a related background) interested in the design, development, and analysis of novel machine learning methods. The positions are situated within the Munich Center for Machine Learning (MCML), one of the national competence centers for Machine Learning in Germany.
25.11.2020
Accepted paper at EDBT 2021
KISS - A fast kNN-based Importance Score for Subspaces
A. Beer, E. Allerborn, V. Hartmann, T. Seidl
06.11.2020
Accepted paper at NeurIPS 2020 Workshop QTNML 2020
A Variational Quantum Circuit Model for Knowledge Graph Embeddings
Y. Ma, V. Tresp
06.11.2020
Accepted paper at WI-IAT 2020
Ada-LLD: Adaptive Node Similarity Using Multi-Scale Local Label Distributions
E. Faerman, F. Borutta, J. Busch, M. Schubert
06.11.2020
Accepted short paper at WI-IAT 2020
Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation
S. Obermeier, M. Berrendorf, P. Kröger
06.11.2020
Accepted paper at NeurIPS 2020 Workshop SSL 2020
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering
J. Busch, E. Faerman, M. Schubert, T. Seidl
06.11.2020
Accepted short paper at WI-IAT 2020
Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods
M. Berrendorf, E. Faerman, L. Vermue, V. Tresp
15.10.2020
Invited presentation at 1st CIKM 2020 Workshop on Combining Symbolic and Sub-symbolic Methods and their Applications (CSSA-CIKM 2020)
Learning with Temporal Knowledge Graphs
Y. Ma, Z. Han, V. Tresp
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 2020
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.
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
on 29.07.2020 with over 20 presentations by our PhD students on current research topics.
02.07.2020
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
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
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.
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