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Autor:
Strotherm, Janine, Hammer, Barbara
Publikováno v:
This work was first published in the proceedings of the 17th International Work-Conference on Artificial Neural Networks (IWANN) in volume 14134 of Lecture Notes in Computer Science, pages 119--133, by Springer Nature in 2023
As relevant examples such as the future criminal detection software [1] show, fairness of AI-based and social domain affecting decision support tools constitutes an important area of research. In this contribution, we investigate the applications of
Externí odkaz:
http://arxiv.org/abs/2410.13296
Autor:
Vaquet, Valerie, Hinder, Fabian, Artelt, André, Ashraf, Inaam, Strotherm, Janine, Vaquet, Jonas, Brinkrolf, Johannes, Hammer, Barbara
Research on methods for planning and controlling water distribution networks gains increasing relevance as the availability of drinking water will decrease as a consequence of climate change. So far, the majority of approaches is based on hydraulics
Externí odkaz:
http://arxiv.org/abs/2410.12461
Autor:
Störck, Felix, Hinder, Fabian, Brinkrolf, Johannes, Paassen, Benjamin, Vaquet, Valerie, Hammer, Barbara
Fairness is an important objective throughout society. From the distribution of limited goods such as education, over hiring and payment, to taxes, legislation, and jurisprudence. Due to the increasing importance of machine learning approaches in all
Externí odkaz:
http://arxiv.org/abs/2410.12452
Autor:
Muschalik, Maximilian, Baniecki, Hubert, Fumagalli, Fabian, Kolpaczki, Patrick, Hammer, Barbara, Hüllermeier, Eyke
Originally rooted in game theory, the Shapley Value (SV) has recently become an important tool in machine learning research. Perhaps most notably, it is used for feature attribution and data valuation in explainable artificial intelligence. Shapley I
Externí odkaz:
http://arxiv.org/abs/2410.01649
In recent studies, line search methods have been demonstrated to significantly enhance the performance of conventional stochastic gradient descent techniques across various datasets and architectures, while making an otherwise critical choice of lear
Externí odkaz:
http://arxiv.org/abs/2407.20650
Autor:
Artelt, André, Hammer, Barbara
EXplainable AI (XAI) constitutes a popular method to analyze the reasoning of AI systems by explaining their decision-making, e.g. providing a counterfactual explanation of how to achieve recourse. However, in cases such as unexpected explanations, t
Externí odkaz:
http://arxiv.org/abs/2406.03012
Autor:
Artelt, André, Kyriakou, Marios S., Vrachimis, Stelios G., Eliades, Demetrios G., Hammer, Barbara, Polycarpou, Marios M.
Drinking water is a vital resource for humanity, and thus, Water Distribution Networks (WDNs) are considered critical infrastructures in modern societies. The operation of WDNs is subject to diverse challenges such as water leakages and contamination
Externí odkaz:
http://arxiv.org/abs/2406.02078
Autor:
Fumagalli, Fabian, Muschalik, Maximilian, Kolpaczki, Patrick, Hüllermeier, Eyke, Hammer, Barbara
The Shapley value (SV) is a prevalent approach of allocating credit to machine learning (ML) entities to understand black box ML models. Enriching such interpretations with higher-order interactions is inevitable for complex systems, where the Shaple
Externí odkaz:
http://arxiv.org/abs/2405.10852
Autor:
Internò, Christian, Raponi, Elena, van Stein, Niki, Bäck, Thomas, Olhofer, Markus, Jin, Yaochu, Hammer, Barbara
The rapid proliferation of smart devices coupled with the advent of 6G networks has profoundly reshaped the domain of collaborative machine learning. Alongside growing privacy-security concerns in sensitive fields, these developments have positioned
Externí odkaz:
http://arxiv.org/abs/2405.10271
Several related works have introduced Koopman-based Machine Learning architectures as a surrogate model for dynamical systems. These architectures aim to learn non-linear measurements (also known as observables) of the system's state that evolve by a
Externí odkaz:
http://arxiv.org/abs/2405.06425