Cedric Kulbach (M.Sc.)

Werdegang

Cedric Kulbach studierte Wirtschaftsingenieurwesen am Karlsruher Institut für Technologie (KIT) mit den Schwerpunkten Operations Research und Simulation, sowie am Institut Polytechnique de Grenoble (INP) mit dem Schwerpunkt Produktentwicklung. Seine Masterarbeit über die integrierte Simulation und Optimierung von Zuliefernetzwerken am Beispiel der Bugatti Automobiles S.A.S. fertigte er in Zusammenarbeit mit dem Institut für Fördertechnik und Logistiksysteme (IFL), dem Institut Polytechnique de Grenoble und der Bugatti Automobiles S.A.S. an. 

Seit August 2018 ist er in dem Forschungsbereich Information Process Engineering (IPE) tätig und beschäftigt sich hauptsächlich mit den Themen automatisiertes Machine Learning, Pipeline Learning und dessen Möglichkeiten zur Personalisierung

Publications

Conference Proceedings (2)

1. Personalized Automated Machine LearningInfoDetails

   Cedric Kulbach, Patrick Philipp, Steffen Thoma, 2020

   Automated Machine Learning (AutoML) is the challenge of finding machine learning models with high predictive performance without the need for specialized data scientists. Existing approaches optimize a pipeline of pre-processing, feature engineering, model selection and hyperparameter optimization, and assume that the user is fully aware of the choice of the underlying metric (such as precision, recall or F1-measure). However, end-users are often unaware of the actual implications of choosing a metric, as the resulting models often significantly vary in their predictions. In this work, we propose a framework to personalise AutoML for individual end-users by learning a designated ranking model from pairwise user preferences and using the latter as the metric function for state-of-the-art AutoML systems. Given a set of possible metrics, we generate candidate models by repeatedly running AutoML with combinations of the former and have the user choose between pairs of resulting models. We use RankNet to learn a personalized ranking function for the end-user, which is used as loss function for final run of a standard AutoML system. To evaluate our proposed framework we define three preferences a user could pursue and show that a ranking model is able to learn these preferences from pairwise comparisons. Furthermore, by changing the metric function of AutoML we show that a personalized preference is able to improve machine learning pipelines. We evaluated the ability of learning a personalized preference and the entire framework on several OpenML multi-class classification datasets.

2. Improving NLU Training over Linked Data with Placeholder ConceptsInfoDetails

   T. Schmitt, C. Kulbach, Y. Sure-Vetter , 2019

   Conversational systems, also known as dialogue systems, have become increasingly popular. They can perform a variety of tasks e.g. in B2C areas such as sales and customer services. A significant amount of research has already been conducted on improving the underlying algorithms of the natural language understanding (NLU) component of dialogue systems. This paper presents an approach to generate training datasets for the NLU component from Linked Data resources. We analyze how differently designed training datasets can impact the performance of the NLU component. As core contribution we introduce and evaluate the performance of different placeholder concepts. Our results show that a trained model with placeholder concepts is capable of handling dynamic Linked Data without retraining the NLU component. Thus, our approach also contributes to the robustness of the NLU component.

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Kontakt

Telefon: +49 721 9654-808
E-Mail: kulbach@dont-want-spam.fzi.de

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