Unreflected Use of Tabular Data Repositories Can Undermine Research Quality

no code implementations • 12 Mar 2025 • Andrej Tschalzev, Lennart Purucker, Stefan Lüdtke, Frank Hutter, Christian Bartelt, Heiner Stuckenschmidt

Data repositories have accumulated a large number of tabular datasets from various domains.

Model Selection

A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data

1 code implementation • 2 Jul 2024 • Andrej Tschalzev, Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

Our framework is available under: https://github. com/atschalz/dc_tabeval.

Feature Engineering Hyperparameter Optimization +2

Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods

1 code implementation • 1 Jul 2024 • Andrej Tschalzev, Paul Nitschke, Lukas Kirchdorfer, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

Neural networks often assume independence among input data samples, disregarding correlations arising from inherent clustering patterns in real-world datasets (e. g., due to different sites or repeated measurements).

Clustering Multi-class Classification

Explaining Neural Networks without Access to Training Data

1 code implementation • 10 Jun 2022 • Sascha Marton, Stefan Lüdtke, Christian Bartelt, Andrej Tschalzev, Heiner Stuckenschmidt

We consider generating explanations for neural networks in cases where the network's training data is not accessible, for instance due to privacy or safety issues.