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10.09.2024

Teaser image to MCML at ECML-PKDD 2024

 

MCML at ECML-PKDD 2024

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024). Vilnius, Lithuania, September 09-13, 2024

We are happy to announce that MCML researchers are represented with 9 papers at ECML-PKDD 2024:

H. Baniecki, G. Casalicchio, B. Bischl and P. Biecek.
On the Robustness of Global Feature Effect Explanations.
DOI.
C. Damke and E. Hüllermeier.
CUQ-GNN: Committee-Based Graph Uncertainty Quantification Using Posterior Networks.
DOI.
R. Fischer, M. Wever, S. Buschjäger and T. Liebig.
MetaQuRe: Meta-learning from Model Quality and Resource Consumption.
DOI.
S. Gilhuber, A. Beer, Y. Ma and T. Seidl.
FALCUN: A Simple and Efficient Deep Active Learning Strategy.
DOI.
P. Jahn, C. M. M. Frey, A. Beer, C. Leiber and T. Seidl.
Data with Density-Based Clusters: A Generator for Systematic Evaluation of Clustering Algorithms.
DOI. GitHub.
Y. Liu, E. Nie, S. Feng, Z. Hua, Z. Ding, D. Wang, Y. Zhang and H. Schütze.
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation.
DOI. GitHub.
M. Muschalik, F. Fumagalli, B. Hammer and E. Hüllermeier.
Explaining Change in Models and Data with Global Feature Importance and Effects. (Workshop paper).
PDF.
F. Stermann, I. Chalkidis, A. Vahidi, B. Bischl and M. Rezaei.
Attention-Driven Dropout: A Simple Method to Improve Self-supervised Contrastive Sentence Embeddings.
DOI.
A. Vahidi, L. Wimmer, H. A. Gündüz, B. Bischl, E. Hüllermeier and M. Rezaei.
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning.
DOI.

10.09.2024

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