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Prof. Dr.
Wrobel, Stefan
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PublicationDecision Snippet Features( 2021)
;Welke, Pascal ;Alkhoury, FouadDecision trees excel at interpretability of their prediction results. To achieve required prediction accuracies, however, often large ensembles of decision trees random forests are considered, reducing interpretability due to large size. Additionally, their size slows down inference on modern hardware and restricts their applicability in lowmemory embedded devices. We introduce Decision Snippet Features, which are obtained from small subtrees that appear frequently in trained random forests. We subsequently show that linear models on top of these features achieve comparable and sometimes even better predictive performance than the original random forest, while reducing the model size by up to two orders of magnitude. 
PublicationMaximum Margin Separations in Finite Closure Systems( 2021)
;Seiffahrt, Florian ;HorvÃ¡rth, TamÃ¡sMonotone linkage functions provide a measure for proximities between elements and subsets of a ground set. Combining this notion with Vapniks idea of support vector machines, we extend the concepts of maximal closed set and halfspace separation in finite closure systems to those with maximum margin. In particular, we define the notion of margin for finite closure systems by means of monotone linkage functions and give a greedy algorithm computing a maximum margin closed set separation for two sets efficiently. The output closed sets are maximum margin halfspaces, i.e., form a partitioning of the ground set if the closure system is Kakutani. We have empirically evaluated our approach on different synthetic datasets. In addition to binary classification of finite subsets of the Euclidean space, we considered also the problem of vertex classification in graphs. Our experimental results provide clear evidence that maximal closed set separation with maximum margin results in a much better predictive performance than that with arbitrary maximal closed sets. 
PublicationMaximal Closed Set and HalfSpace Separations in Finite Closure Systems( 2020)
;Seiffarth, Florian ;HorvÃ¡th, TÃ¡masMotivated by various binary classification problems in structured data (e.g., graphs or other relational and algebraic structures), we investigate some algorithmic properties of closed set and halfspace separation in abstract closure systems. Assuming that the underlying closure system is finite and given by the corresponding closure operator, we formulate some negative and positive complexity results for these two separation problems. In particular, we prove that deciding halfspace separability in abstract closure systems is NPcomplete in general. On the other hand, for the relaxed problem of maximal closed set separation we propose a simple greedy algorithm and show that it is efficient and has the best possible lower bound on the number of closure operator calls. As a second direction to overcome the negative result above, we consider Kakutani closure systems and show first that our greedy algorithm provides an algorithmic characterization of this kind of set systems. As one of the major potential application fields, we then focus on Kakutani closure systems over graphs and generalize a fundamental characterization result based on the Pasch axiom to graph structure partitioning of finite sets. Though the primary focus of this work is on the generality of the results obtained, we experimentally demonstrate the practical usefulness of our approach on vertex classification in different graph datasets. 
PublicationTrustworthy Use of Artificial Intelligence( 201907)
;Cremers, Armin B. ;Englander, Alex ;Gabriel, Markus ;Rostalski, Frauke ;Volmer, Julia ;Voosholz, JanThis publication forms a basis for the interdisciplinary development of a certification system for artificial intelligence. In view of the rapid development of artificial intelligence with disruptive and lasting consequences for the economy, society, and everyday life, it highlights the resulting challenges that can be tackled only through interdisciplinary dialog between IT, law, philosophy, and ethics. As a result of this interdisciplinary exchange, it also defines six AIspecific audit areas for trustworthy use of artificial intelligence. They comprise fairness, transparency, autonomy and control, data protection as well as security and reliability while addressing ethical and legal requirements. The latter are further substantiated with the aim of operationalizability. 
PublicationVertrauenswÃ¼rdiger Einsatz von KÃ¼nstlicher Intelligenz(Fraunhofer IAIS, 2019)
;Cremers, Armin B. ;Englander, Alex ;Gabriel, Markus ;Mock, M. ;Rostalski, Frauke ;Volmer, Julia ;Voosholz, JanDie vorliegende Publikation dient als Grundlage fÃ¼r die interdisziplinÃ¤re Entwicklung einer Zertifizierung von KÃ¼nstlicher Intelligenz. Angesichts der rasanten Entwicklung von KÃ¼nstlicher Intelligenz mit disruptiven und nachhaltigen Folgen fÃ¼r Wirtschaft, Gesellschaft und Alltagsleben verdeutlicht sie, dass sich die hieraus ergebenden Herausforderungen nur im interdisziplinÃ¤ren Dialog von Informatik, Rechtswissenschaften, Philosophie und Ethik bewÃ¤ltigen lassen. Als Ergebnis dieses interdisziplinÃ¤ren Austauschs definiert sie zudem sechs KIspezifische Handlungsfelder fÃ¼r den vertrauensvollen Einsatz von KÃ¼nstlicher Intelligenz: Sie umfassen Fairness, Transparenz, Autonomie und Kontrolle, Datenschutz sowie Sicherheit und VerlÃ¤sslichkeit und adressieren dabei ethische und rechtliche Anforderungen. Letztere werden mit dem Ziel der Operationalisierbarkeit weiter konkretisiert. 
PublicationUsing echo state networks for cryptography( 2017)
;Ramamurthy, Rajkumar ;Buza, KrisztianEcho state networks are simple recurrent neural networks that are easy to implement and train. Despite their simplicity, they show a form of memory and can predict or regenerate sequences of data. We make use of this property to realize a novel neural cryptography scheme. The key idea is to assume that Alice and Bob share a copy of an echo state network. If Alice trains her copy to memorize a message, she can communicate the trained part of the network to Bob who plugs it into his copy to regenerate the message. Considering a bytelevel representation of in and output, the technique applies to arbitrary types of data (texts, images, audio files, etc.) and practical experiments reveal it to satisfy the fundamental cryptographic properties of diffusion and confusion.