25.09.2023

MCML Researchers With Three Papers at ECAI 2023
26th European Conference on Artificial Intelligence (ECAI 2023). Kraków, Poland, 30.09.2023–04.10.2023
We are happy to announce that MCML researchers are represented with three papers at ECAI 2023. Congrats to our researchers!
Main Track (3 papers)
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions.
AEQUITAS @ECAI 2023 - 1st Workshop on Fairness and Bias in AI co-located with the 26th European Conference on Artificial Intelligence (ECAI 2023). Kraków, Poland, Sep 30-Oct 04, 2023. PDF
Abstract
A decision can be defined as fair if equal individuals are treated equally and unequals unequally. Adopting this definition, the task of designing machine learning models that mitigate unfairness in automated decision-making systems must include causal thinking when introducing protected attributes. Following a recent proposal, we define individuals as being normatively equal if they are equal in a fictitious, normatively desired (FiND) world, where the protected attribute has no (direct or indirect) causal effect on the target. We propose rank-preserving interventional distributions to define an estimand of this FiND world and a warping method for estimation. Evaluation criteria for both the method and resulting model are presented and validated through simulations and empirical data. With this, we show that our warping approach effectively identifies the most discriminated individuals and mitigates unfairness.
MCML Authors
Statistical Learning and Data Science

Susanne Dandl
Dr.
* Former Member
Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation.
XI-ML @ECAI 2023 - 3rd International Workshop on Explainable and Interpretable Machine Learning co-located with the 26th European Conference on Artificial Intelligence (ECAI 2023). Kraków, Poland, Sep 30-Oct 04, 2023. DOI
Abstract
Surrogate models play a crucial role in retrospectively interpreting complex and powerful black box machine learning models via model distillation. This paper focuses on using model-based trees as surrogate models which partition the feature space into interpretable regions via decision rules. Within each region, interpretable models based on additive main effects are used to approximate the behavior of the black box model, striking for an optimal balance between interpretability and performance. Four model-based tree algorithms, namely SLIM, GUIDE, MOB, and CTree, are compared regarding their ability to generate such surrogate models. We investigate fidelity, interpretability, stability, and the algorithms’ capability to capture interaction effects through appropriate splits. Based on our comprehensive analyses, we finally provide an overview of user-specific recommendations.
MCML Authors

Julia Herbinger
Dr.
* Former Member

Susanne Dandl
Dr.
* Former Member
Statistical Learning and Data Science
Statistical Learning and Data Science
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
Abstract
Portfolio optimization tasks describe sequential decision problems in which the investor’s wealth is distributed across a set of assets. Allocation constraints are used to enforce minimal or maximal investments into particular subsets of assets to control for objectives such as limiting the portfolio’s exposure to a certain sector due to environmental concerns. Although methods for (CRL) can optimize policies while considering allocation constraints, it can be observed that these general methods yield suboptimal results. In this paper, we propose a novel approach to handle allocation constraints based on a decomposition of the constraint action space into a set of unconstrained allocation problems. In particular, we examine this approach for the case of two constraints. For example, an investor may wish to invest at least a certain percentage of the portfolio into green technologies while limiting the investment in the fossil energy sector. We show that the action space of the task is equivalent to the decomposed action space, and introduce a new (RL) approach CAOSD, which is built on top of the decomposition. The experimental evaluation on real-world Nasdaq data demonstrates that our approach consistently outperforms state-of-the-art CRL benchmarks for portfolio optimization.
MCML Authors
25.09.2023
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