29.09.2023
MCML at ECAI 2023
Three Accepted Papers
1st Workshop on Fairness and Bias in AI co-located with the 26th European Conference on Artificial Intelligence, Kraków, Poland, Sep 30-Oct 04, 2023
We are happy to announce that MCML researchers have contributed a total of 3 papers to ECAI 2023. Congrats to our researchers!
Main Track (3 papers)
L. Bothmann • S. Dandl • M. Schomaker
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions.
ECAI 2023 - 1st Workshop on Fairness and Bias in AI co-located with the 26th European Conference on Artificial Intelligence. Kraków, Poland, Sep 30-Oct 04, 2023. PDF
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions.
ECAI 2023 - 1st Workshop on Fairness and Bias in AI co-located with the 26th European Conference on Artificial Intelligence. Kraków, Poland, Sep 30-Oct 04, 2023. PDF
J. Herbinger • S. Dandl • F. K. Ewald • S. Loibl • G. Casalicchio
Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation.
ECAI 2023 - 3rd International Workshop on Explainable and Interpretable Machine Learning co-located with the 26th European Conference on Artificial Intelligence. Kraków, Poland, Sep 30-Oct 04, 2023. DOI
Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation.
ECAI 2023 - 3rd International Workshop on Explainable and Interpretable Machine Learning co-located with the 26th European Conference on Artificial Intelligence. Kraków, Poland, Sep 30-Oct 04, 2023. DOI
D. Winkel • N. Strauß • M. Schubert • T. Seidl
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning.
ECAI 2023 - 26th European Conference on Artificial Intelligence. Kraków, Poland, Sep 30-Oct 04, 2023. DOI
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning.
ECAI 2023 - 26th European Conference on Artificial Intelligence. Kraków, Poland, Sep 30-Oct 04, 2023. DOI
Related
14.09.2026
Björn Eskofier: The Missing Ingredient for AI in Medicine Is Real-World Data
MCML PI Björn Eskofier explains why real-world data is key to using AI for personalized treatment and more empowered healthcare.
