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ORCIDLudwig Bothmann
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2020 – today
- 2025
[i10]Charlotte Leininger, Simon Rittel, Ludwig Bothmann:
Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective. CoRR abs/2501.14710 (2025)- 2024
- [j3]Ludwig Bothmann
, Kristina Peters:
Fairness als Qualitätskriterium im Maschinellen Lernen - Rekonstruktion des philosophischen Konzepts und Implikationen für die Nutzung außergesetzlicher Merkmale bei qualifizierten Mietspiegeln. AStA Wirtschafts und Sozialstatistisches Arch. 18(2): 185-201 (2024) - [c7]Benoît Ronval, Siegfried Nijssen, Ludwig Bothmann:
Can Generative Ai-based Data Balancing Mitigate Unfairness Issues in Machine Learning? EWAF 2024 - [c6]Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer:
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? ICML 2024 - [c5]Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann:
mlr3summary: Concise and interpretable summaries for machine learning models. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 281-288 - [c4]Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König:
A Guide to Feature Importance Methods for Scientific Inference. xAI (2) 2024: 440-464 - [i9]Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer:
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? CoRR abs/2402.01484 (2024) - [i8]Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König:
A Guide to Feature Importance Methods for Scientific Inference. CoRR abs/2404.12862 (2024) - [i7]Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann:
mlr3summary: Concise and interpretable summaries for machine learning models. CoRR abs/2404.16899 (2024) - 2023
- [j2]Ludwig Bothmann, Lisa Wimmer, Omid Charrakh, Tobias Weber, Hendrik Edelhoff, Wibke Peters, Hien Nguyen, Caryl Benjamin, Annette Menzel:
Automated wildlife image classification: An active learning tool for ecological applications. Ecol. Informatics 77: 102231 (2023) - [c3]Ludwig Bothmann, Susanne Dandl, Michael Schomaker:
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions. AEQUITAS@ECAI 2023 - [c2]Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann:
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. ECML/PKDD (3) 2023: 479-495 - [i6]Ludwig Bothmann, Lisa Wimmer, Omid Charrakh, Tobias Weber, Hendrik Edelhoff, Wibke Peters, Hien Nguyen, Caryl Benjamin, Annette Menzel:
Automated wildlife image classification: An active learning tool for ecological applications. CoRR abs/2303.15823 (2023) - [i5]Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann:
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. CoRR abs/2305.02780 (2023) - [i4]Ludwig Bothmann, Susanne Dandl, Michael Schomaker:
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions. CoRR abs/2307.12797 (2023) - [i3]Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig Bothmann, Bernd Bischl, Thomas Augustin, Anne-Laure Boulesteix:
Evaluating machine learning models in non-standard settings: An overview and new findings. CoRR abs/2310.15108 (2023) - 2022
- [j1]Wael Ghada, Éric Casellas, Julia Herbinger, Albert Garcia-Benadí, Ludwig Bothmann, Nicole Estrella, Joan Bech, Annette Menzel:
Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar. Remote. Sens. 14(18): 4563 (2022) - [c1]Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl:
Developing Open Source Educational Resources for Machine Learning and Data Science. Teaching ML 2022: 1-6 - [i2]Ludwig Bothmann, Kristina Peters, Bernd Bischl:
What Is Fairness? Implications For FairML. CoRR abs/2205.09622 (2022) - 2021
- [i1]Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl:
Developing Open Source Educational Resources for Machine Learning and Data Science. CoRR abs/2107.14330 (2021)
2010 – 2019
- 2016
- [b1]Ludwig Bothmann:
Efficient statistical analysis of video and image data. Ludwig Maximilian University of Munich, Germany, 2016
Coauthor Index
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