Runner-Up Prize at AutoML 2026 for Tabular-DC-Bench

At the International Conference on Automated Machine Learning (AutoML 2026), the paper “Tabular-DC-Bench” received the runner-up prize in the ABCD track (Applications, Benchmarks, Challenges, and Datasets). The work was presented by MCML Junior Member Mamdouh Aljoud. It was co-authored by MCML Junior Member Gabriel Tavares, Sandra Gilhuber, Collin Leiber, and MCML Director Thomas Seidl.

Tabular-DC-Bench is a meta-dataset for hyperparameter optimization in deep clustering on tabular data. It comprises 375,000 runs across 100 datasets and five deep clustering algorithms. The results show that a deep clustering method with its default configuration often performs worse than K-Means, while the best configuration usually outperforms it. At the same time, internal metrics such as the Silhouette score cannot reliably indicate which configuration has been found. The meta-dataset is publicly available on Zenodo and GitHub, so others can build on it.

Congratulations to the authors on this recognition!