15.09.2023

Teaser image to MCML at ECML-PKDD 2023

MCML at ECML-PKDD 2023

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023). Turin, Italy, September 18-22, 2023

We are happy to announce that MCML researchers are represented with 12 papers at ECML-PKDD 2023:

M. Aßenmacher, L. Rauch, J. Goschenhofer, A. Stephan, B. Bischl, B. Roth and B. Sick.
Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. (Workshop paper).
URL.
S. Dandl, G. Casalicchio, B. Bischl and L. Bothmann.
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations.
DOI.
S. Gilhuber, J. Busch, D. Rotthues, C. M. M. Frey and T. Seidl.
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification.
DOI.
S. Gilhuber, R. Hvingelby, M. L. A. Fok and T. Seidl.
How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning.
DOI.
S. Haas and E. Hüllermeier.
Rectifying Bias in Ordinal Observational Data Using Unimodal Label Smoothing.
DOI.
T. Kaufmann, S. Ball, J. Beck, F. Kreuter and E. Hüllermeier.
On the Challenges and Practices of Reinforcement Learning from Real Human Feedback. (Workshop paper).
PDF.
M. Klein, C. Leiber and C. Böhm.
k-SubMix: Common Subspace Clustering on Mixed-Type Data.
DOI.
M. Muschalik, F. Fumagalli, B. Hammer and E. Hüllermeier.
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.
DOI.
I. T. Öztürk, R. Nedelchev, C. Heumann, E. Garces Arias, M. Roger, B. Bischl and M. Aßenmacher.
How Different Is Stereotypical Bias Across Languages?. (Workshop paper).
arXiv.
L. Rauch, M. Aßenmacher, D. Huseljic, M. Wirth, B. Bischl and B. Sick.
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers.
DOI.
S. Urchs, V. Thurner, M. Aßenmacher, C. Heumann and S. Thiemichen.
How Prevalent is Gender Bias in ChatGPT? - Exploring German and English ChatGPT Responses. (Workshop paper).
arXiv.
J. G. Wiese, L. Wimmer, T. Papamarkou, B. Bischl, S. Günnemann and D. Rügamer.
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.
DOI.

15.09.2023


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