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Christian Internó, Elena Raponi, Niki van Stein, Thomas Bäck, Markus Olhofer, Yaochu Jin, Barbara Hammer , "Automated Federated Learning via Informed Pruning", International Conference on Automated Machine Learning (AUTOML 24), 2024.
AbstractFederated learning (FL) represents a pivotal shift in machine learning (ML) as it enables collaborative training of local ML models coordinated by a central aggregator, all without the need to exchange local data. However, its application on edge devices is hindered by limited computational capabilities and data communication challenges, compounded by the inherent complexity of Deep Learning (DL) models. Model pruning is identified as a key tec...
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Lydia Fischer and Patricia Wollstadt , "Precision and Recall Reject Curves for Classification", WSOM, 2024.
AbstractFor some classification scenarios, it is desirable to use only those classification instances that a trained model associates with a high certainty. To obtain such high-certainty instances, previous work has proposed accuracy-reject curves. Reject curves allow to evaluate and compare the performance of different certainty measures over a range of thresholds for accepting or rejecting classifications. However, the accuracy may not be the most sui...
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Duc Anh Nguyen , "Efficient tuning of automated machine learning pipelines", Leiden University, 2024.
AbstractAutoML has attracted community attention due to its success in shortening the machine learning development cycle for real-world applications. Optimization plays a crucial role in AutoML frameworks by helping to identify a fine-tuned ML pipeline that suits a given practical problem. Several state-of-the-art optimization approaches, including Bayesian optimization, Bandit learning, and Racing procedures, have been proposed to enhance the performanc...
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Johannes Varga, Guenther Raidl, Elina Rönnberg, Tobias Rodemann , "Scheduling jobs using queries to interactively learn human availability times", Computers & Operations Research, vol. 167, pp. 106648, 2024.
AbstractThe solution to a job scheduling problem that involves humans as well some other shared resource has to consider the humans’ availability times. For practical acceptance of a scheduling tool, it is crucial that the interaction with the humans is kept simple and to a minimum. It is rarely practical to ask users to fully specify their availability times or to let them enumerate all possible starting times for their jobs. In the scenario we are con...
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Daniel Tanneberg, Felix Ocker, Stephan Hasler, Jörg Deigmöller, Anna Belardinelli, Chao Wang, Heiko Wersing, Bernhard Sendhoff, Michael Gienger , "To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions", Late Breaking Work Poster - IEEE International Conference on Robotics and Automation (ICRA), 2024.
AbstractHumans are inherently social beings. To seamlessly integrate robots into our daily lives, it is crucial that they can engage in multiparty interactions effectively and supportively, without disrupting group dynamics. Hence, we asked “How can a robot provide unobtrusive physical support within a group of humans?”...
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Tim Puphal, Ryohei Hirano, Akihito Kimata, Julian Eggert , "Reducing Warning Errors in Driver Support with Personalized Risk Maps", IEEE International Conference on Vehicular Electronics and Safety 2024, 2024.
AbstractWe consider the problem of human-focused driver support. State-of-the-art personalization concepts allow to estimate parameters for vehicle control systems or driver models. However, there are currently few approaches proposed that use personalized models and evaluate the effectiveness in the form of general risk warning. In this paper, we therefore propose a warning system that estimates a personalized risk factor for the given driver based on t...
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Helen Beierling, Phillip Richter, Mara Brandt, Lutz Terfloth, Carsten Schulte, Heiko Wersing, Anna-Lisa Vollmer , "What you need to know about a learning robot: Identifying the enabling architecture of complex systems", Cognitive Systems Research, no. 88, 2024.
AbstractNowadays we deal with robots and AI more and more in our everyday life. However, their behavior is not always apparent to most lay users, especially in error situations. This can lead to misconceptions about the behavior of the technologies being used. This in turn can lead to misuse and rejection by users. Explanation, for example through transparency, can address these misconceptions. However, explaining the entire software or hardware woul...
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Mitra Baratchi, Can Wang, Steffen Limmer, Jan van Rijn, Holger Hoos, Thomas Bäck, Markus Olhofer , "Automated Machine Learning: Past, Present and Future", Artificial Intelligence Review, 2024.
AbstractAutomated Machine Learning (AutoML) is a young research area aiming at making high-performance machine learning techniques accessible to a broad set of users. This is achieved by identifying all design choices in creating a machine-learning model and addressing them automatically to generate performance-optimised models. In this article, we provide an extensive overview of the past and present, as well as future perspectives of AutoML. First, we ...
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Chao Wang, Stephan Hasler, Daniel Tanneberg, Felix Ocker, Frank Joublin, Antonello Ceravola, Jörg Deigmöller, Michael Gienger , "LAMI: Large Language Models for Multi-Modal Human-Robot Interaction (CHI'24 workshop position paper)", CHI 2024 workshop, 2024.
AbstractThis paper presents an innovative large language model (LLM)-based robotic system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI systems relied on complex designs for intent estimation, reasoning, and behavior generation, which were resource-intensive. In contrast, our system empowers researchers and practitioners to regulate robot behavior through three key aspects: providing high-level linguistic guidance, creating "at...
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Chao Wang, Stephan Hasler, Daniel Tanneberg, Felix Ocker, Frank Joublin, Antonello Ceravola, Jörg Deigmöller, Michael Gienger , "LaMI: Large Language Models for Multi-Modal Human-Robot Interaction ", CHI 2024, 2024.
AbstractIn current approaches for designing human-robot interaction, engineers specialized in the field of robotics establish rules based on the context of an application scenario and a multimodal input from a user in order to define how the robot should react in the specific situation, and to generate an output accordingly. This represents a challenging task, as manually setting up the robot's interactive behavior in a specific situation is complex and ...
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