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Prof. Dr.
Wrobel, Stefan
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PublicationRobustness in Fatigue Strength Estimation( 20221202)Fatigue strength estimation is a costly manual material characterization process in which stateoftheart approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learningbased approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a reallife lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the stateoftheart methods, potentially reducing the number of costly experiments.

PublicationA Fast Heuristic for Computing Geodesic Closures in Large Networks( 20221106)
;Seiffarth, FlorianMotivated by the increasing interest in applications of graph geodesic convexity in machine learning and data mining, we present a heuristic for approximating the geodesic convex hull of node sets in large networks. It generates a small set of (almost) maximal outerplanar spanning subgraphs for the input graph, computes the geodesic closure in each of these graphs, and regards a node as an element of the convex hull if it belongs to the closed sets for at least a user specified number of outerplanar graphs. Our heuristic algorithm runs in time linear in the number of edges of the input graph, i.e., it is faster with one order of magnitude than the standard algorithm computing the closure exactly. Its performance is evaluated empirically by approximating convexity based coreperiphery decomposition of networks. Our experimental results with large realworld networks show that for most networks, the proposed heuristic was able to produce close approximations significantly faster than the standard algorithm computing the exact convex hulls. For example, while our algorithm calculated an approximate coreperiphery decomposition in 5 h or less for networks with more than 20 million edges, the standard algorithm did not terminate within 50 days. 
PublicationWasserstein Dropout( 20220908)
;Sicking, Joachim ;Pintz, Maximilian AlexanderFischer, AsjaDespite of its importance for safe machine learning, uncertainty quantification for neural networks is far from being solved. Stateoftheart approaches to estimate neural uncertainties are often hybrid, combining parametric models with explicit or implicit (dropoutbased) ensembling. We take another pathway and propose a novel approach to uncertainty quantification for regression tasks, Wasserstein dropout, that is purely nonparametric. Technically, it captures aleatoric uncertainty by means of dropoutbased subnetwork distributions. This is accomplished by a new objective which minimizes the Wasserstein distance between the label distribution and the model distribution. An extensive empirical analysis shows that Wasserstein dropout outperforms stateoftheart methods, on vanilla test data as well as under distributional shift in terms of producing more accurate and stable uncertainty estimates. 
PublicationThe why and how of trustworthy AI( 20220903)Artificial intelligence is increasingly penetrating industrial applications as well as areas that affect our daily lives. As a consequence, there is a need for criteria to validate whether the quality of AI applications is sufficient for their intended use. Both in the academic community and societal debate, an agreement has emerged under the term “trustworthiness” as the set of essential quality requirements that should be placed on an AI application. At the same time, the question of how these quality requirements can be operationalized is to a large extent still open. In this paper, we consider trustworthy AI from two perspectives: the product and organizational perspective. For the former, we present an AIspecific risk analysis and outline how verifiable arguments for the trustworthiness of an AI application can be developed. For the second perspective, we explore how an AI management system can be employed to assure the trustworthiness of an organization with respect to its handling of AI. Finally, we argue that in order to achieve AI trustworthiness, coordinated measures from both product and organizational perspectives are required.

PublicationData Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning( 20220722)Machine learning and artificial intelligence have become crucial factors for the competitiveness of individual companies and entire economies. Yet their successful deployment requires access to a large volume of training data often not even available to the largest corporations. The rise of trustworthy federated digital ecosystems will significantly improve data availability for all participants and thus will allow a quantum leap for the widespread adoption of artificial intelligence at all scales of companies and in all sectors of the economy. In this chapter, we will explain how AI systems are built with data science and machine learning principles and describe how this leads to AI platforms. We will detail the principles of distributed learning which represents a perfect match with the principles of distributed data ecosystems and discuss how trust, as a central value proposition of modern ecosystems, carries over to creating trustworthy AI systems.

PublicationA generalized WeisfeilerLehman graph kernel( 20220427)
;Schulz, Till Hendrik ;Welke, PascalAfter more than one decade, WeisfeilerLehman graph kernels are still among the most prevalent graph kernels due to their remarkable predictive performance and time complexity. They are based on a fast iterative partitioning of vertices, originally designed for deciding graph isomorphism with onesided error. The WeisfeilerLehman graph kernels retain this idea and compare such labels with respect to equality. This binary valued comparison is, however, arguably too rigid for defining suitable graph kernels for certain graph classes. To overcome this limitation, we propose a generalization of WeisfeilerLehman graph kernels which takes into account a more natural and finer grade of similarity between WeisfeilerLehman labels than equality. We show that the proposed similarity can be calculated efficiently by means of the Wasserstein distance between certain vectors representing WeisfeilerLehman labels. This and other facts give rise to the natural choice of partitioning the vertices with the Wasserstein kmeans algorithm. We empirically demonstrate on the WeisfeilerLehman subtree kernel, which is one of the most prominent WeisfeilerLehman graph kernels, that our generalization significantly outperforms this and other stateoftheart graph kernels in terms of predictive performance on datasets which contain structurally more complex graphs beyond the typically considered molecular graphs. 
PublicationVisual Analytics for HumanCentered Machine Learning( 20220125)
;Andrienko, Natalia ;Andrienko, Gennady ;Adilova, LinaraWe introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, first, a conceptual mismatch between ML/XAI outputs and human mental models and ways of reasoning, and second, a mismatch between the information quantity and level of detail and human capabilities to perceive and understand. A grand challenge is to adapt ML and XAI to human goals, concepts, values, and ways of thinking. Complementing the current efforts in XAI towards solving this challenge, VA can contribute by exploiting the potential of visualization as an effective way of communicating information to humans and a strong trigger of human abstractive perception and thinking. We propose a crossdisciplinary research framework and formulate research directions for VA. 
PublicationA Simple Heuristic for the Graph Tukey Depth Problem with Potential Applications to Graph Mining( 2022)
;Seiffarth, FlorianWe study a recently introduced adaptation of Tukey depth to graphs and discuss its algorithmic properties and potential applications to mining and learning with graphs. In particular, since it is NPhard to compute the Tukey depth of a node, as a first contribution we provide a simple heuristic based on maximal closed set separation in graphs and show empirically on different graph datasets that its approximation error is small. Our second contribution is concerned with geodesic coreperiphery decompositions of graphs. We show empirically that the geodesic core of a graph consists of those nodes that have a high Tukey depth. This information allows for a parameterized deterministic definition of the geodesic core of a graph. 
PublicationLearning Weakly Convex Sets in Metric Spaces( 20210910)
;Stadtländer, EikeWe introduce the notion of weak convexity in metric spaces, a generalization of ordinary convexity commonly used in machine learning. It is shown that weakly convex sets can be characterized by a closure operator and have a unique decomposition into a set of pairwise disjoint connected blocks. We give two generic efficient algorithms, an extensional and an intensional one for learning weakly convex concepts and study their formal properties. Our experimental results concerning vertex classification clearly demonstrate the excellent predictive performance of the extensional algorithm. Two nontrivial applications of the intensional algorithm to polynomial PAClearnability are presented. The first one deals with learning kconvex Boolean functions, which are already known to be efficiently PAClearnable. It is shown how to derive this positive result in a fairly easy way by the generic intensional algorithm. The second one is concerned with the Euclidean space equipped with the Manhattan distance. For this metric space, weakly convex sets form a union of pairwise disjoint axisaligned hyperrectangles. We show that a weakly convex set that is consistent with a set of examples and contains a minimum number of hyperrectangles can be found in polynomial time. In contrast, this problem is known to be NPcomplete if the hyperrectangles may be overlapping. 
PublicationDecision Snippet Features( 20210505)
;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.