Efficient statistical analysis of video and image data. - Ludwig Bothmann (2016)

Developing Open Source Educational Resources for Machine Learning and Data Science. - Ludwig Bothmann; Sven Strickroth; Giuseppe Casalicchio; David Rügamer; Marius Lindauer; Fabian Scheipl; Bernd Bischl (Teaching ML, 2022)

What Is Fairness? Implications For FairML. - Ludwig Bothmann; Kristina Peters; Bernd Bischl (CoRR, 2022)

Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar. - Wael Ghada; Éric Casellas; Julia Herbinger; Albert Garcia-Benadí; Ludwig Bothmann; Nicole Estrella; Joan Bech; Annette Menzel (Remote. Sens., 2022)

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions. - Ludwig Bothmann; Susanne Dandl; Michael Schomaker (AEQUITAS@ECAI, 2023)

Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. - Susanne Dandl; Giuseppe Casalicchio; Bernd Bischl; Ludwig Bothmann (ECML/PKDD (3), 2023)

Evaluating machine learning models in non-standard settings: An overview and new findings. - Roman Hornung; Malte Nalenz; Lennart Schneider; Andreas Bender 001; Ludwig Bothmann; Bernd Bischl; Thomas Augustin 001; Anne-Laure Boulesteix (CoRR, 2023)

Automated wildlife image classification: An active learning tool for ecological applications. - Ludwig Bothmann; Lisa Wimmer; Omid Charrakh; Tobias Weber; Hendrik Edelhoff; Wibke Peters; Hien Nguyen; Caryl Benjamin; Annette Menzel (Ecol. Informatics, 2023)

Can Generative Ai-based Data Balancing Mitigate Unfairness Issues in Machine Learning? - Benoît Ronval; Siegfried Nijssen; Ludwig Bothmann (EWAF, 2024)

Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? - Emanuel Sommer; Lisa Wimmer; Theodore Papamarkou; Ludwig Bothmann; Bernd Bischl; David Rügamer (ICML, 2024)

mlr3summary: Concise and interpretable summaries for machine learning models. - Susanne Dandl; Marc Becker; Bernd Bischl; Giuseppe Casalicchio; Ludwig Bothmann (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2024)

A Guide to Feature Importance Methods for Scientific Inference. - Fiona Katharina Ewald; Ludwig Bothmann; Marvin N. Wright; Bernd Bischl; Giuseppe Casalicchio; Gunnar König (xAI (2), 2024)

Fairness als Qualitätskriterium im Maschinellen Lernen - Rekonstruktion des philosophischen Konzepts und Implikationen für die Nutzung außergesetzlicher Merkmale bei qualifizierten Mietspiegeln. - Ludwig Bothmann; Kristina Peters (AStA Wirtschafts und Sozialstatistisches Arch., 2024)

What is Fairness? On Protected Attributes and Fictitious Worlds. - Ludwig Bothmann; Kristina Peters; Bernd Bischl (EWAF, 2025)

Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective. - Charlotte Leininger; Simon Rittel; Ludwig Bothmann (EWAF, 2025)

Privilege Scores. - Ludwig Bothmann; Philip A. Boustani; José M. Álvarez 002; Giuseppe Casalicchio; Bernd Bischl; Susanne Dandl (CoRR, 2025)

Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning. - Lisa Wimmer; Bernd Bischl; Ludwig Bothmann (CoRR, 2025)

Invariance Pair-Guided Learning: Enhancing Robustness in Neural Networks. - Martin Surner; Abdelmajid Khelil; Ludwig Bothmann (CoRR, 2025)