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Felix Lanfermann, Qiqi Liu, Yaochu Jin, Sebastian Schmitt , "Identification of Energy Management Configuration Concepts from a Set of Pareto-optimal Solutions", Energy Conversion and Management: X, vol. 22, pp. 100576, 2024.
AbstractOptimizing building configurations for an efficient use of energy is increasingly receiving attention by current research and several methods have been developed to address this task. Selecting a suitable configuration based on multiple conflicting objectives, such as initial investment cost, recurring cost, robustness with respect to uncertainty of grid operation is, however, a difficult multi-criteria decision making problem. Concept identifica...
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Felix Lanfermann, Thiago de Jesus de Araujo Rios, Stefan Menzel , "Large Language Model-assisted Clustering and Concept Identification of Engineering Design Data", IEEE Conference on Artificial Intelligence, 2024.
AbstractRecent advances in Large Language Models (LLM) open up opportunities for users to interact with domain spe- cific knowledge and execute (semi-)professional tasks in a dia- log fashion. Without profound knowledge in data science and programming languages, basic statistics and further detailed analyses can be conducted intuitively through natural language prompts. Accessing common data science methods, LLMs can assist users in visualizing, i...
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Svenja Kenneweg, Philipp Cimiano, Jörg Deigmöller, Julian Eggert , "Benchmarking the Ability of Large Language Models to Reason about Event Sequences", Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KEOD), 2024.
AbstractThe ability to reason about events and their temporal relations is a key aspect in Natural Language Understanding. In this paper, we investigate the ability of Large Language Models to resolve temporal references with respect to longer event sequences. Given that events rarely occur in isolation, it is crucial to determine the extent to which Large Language Models can reason about longer sequences of events. Towards this goal, we introduce a nov...
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Frank Joublin and Antonello Ceravola , "Exploration of Generative model at Honda-Research Institute", Meetup at SRH University Heidelberg, 2024.
AbstractIn this talk we present at the Generative AI conference the HRI-EU institute at first, then we recap the evolution of AI in the trends of LLM and their applicability in different domains and products. We touch on the main exposed limitation of LLM and a sample of the different solution the community and the different AI companies came to. We then pick 3 investigated use-cases HRI-EU did on the usage of generative AI: Text to 3D generation in car ...
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Judith Sieker, Nazia Attari, Heiko Wersing, Simeon Schüz, Hendrik Buschmeier, Sina Zarriess , "The Illusion of Competence: Evaluating the Effect of Explanations On Users' Mental Models of Visual Question Answering Systems", The 2024 Conference on Empirical Methods in Natural Language Processing, 2024.
AbstractIn our study, we examine how participants/users perceive the limitations of an AI system when it encounters tasks it cannot perform perfectly. Our objective is to investigate whether providing explanations alongside model answers aids users in building an appropriate mental model of an AI's limitations. To accomplish this, we employ a visual question explanation task and evaluate both the accuracy of the models' answers and the effectiveness of t...
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Angus Kenny, Tapabrata Ray, Steffen Limmer, Hemant Kumar Singh, Tobias Rodemann, Markus Olhofer , "A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search Behaviour", Evostar 2024, 2024.
AbstractIn this study, the challenge of developing a dissimilarity metric for machine learning pipeline optimization is addressed. Traditional approaches, limited by simplified operator sets and pipeline structures, fail to address the full complexity of this task. Two novel metrics are proposed for measuring structural, and hyperparameter, dissimilarity in the decision space. A hierarchical approach is employed to integrate these metrics, prioritizing s...
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Christiane Attig, Patricia Wollstadt, Thomas Franke, Tim Schrills, Christiane Wiebel , "More than Task Performance: Developing New Criteria for Successful Human-AI Teaming Using the Cooperative Card Game Hanabi", CHI EA '24: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, no. 245, pp. 1-11, 2024.
AbstractAs we shift to designing AI agents as teammates rather than tools, the social aspects of human-AI interaction become more pronounced. Consequently, to develop agents that are able to navigate the social dynamics that accompany cooperative teamwork, evaluation criteria that refer only to objective task performance will not be suffi cient. We propose perceived cooperativity and teaming perception as subjective metrics for investigating successfu...
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Christian Internó, Barbara Hammer, Yaochu Jin, Markus Olhofer , "FedLEx: Federated Loss Exploration for Improved Convergence", Machine Learning Summer School in Okinawa 2024, 2024.
AbstractFederated Learning (FL) offers a decentralized machine learning framework, allowing participants to collaboratively train models while keeping data localized. In non-IID settings, where data distribution among clients isn’t consistent, challenges arise that hinder global model convergence and good generalization. To alleviate this, we introduce the Federated Loss Exploration (FedLEx) method. FedLEx incorporates a loss landscape exploration phase ...
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Johannes Varga, Guenther Raidl, Tobias Rodemann , "Selecting User Queries in Interactive Job Scheduling", Eurocast 2024, 19th International Conference on Computer Aided Systems Theory, 2024.
AbstractWe consider a class of job scheduling problems in which human users, e.g., the personnel of a company, need to perform jobs on some shared machines and the availabilities of these users as well as the machines is critical. In such situations it is rarely practical to ask users to fully specify their availability times. Instead we assume users initially only propose a single starting time for each of their jobs, and a feasible and optimized schedu...
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Christian Internó, Markus Olhofer, Yaochu Jin, Barbara Hammer , "Federated Loss Exploration for Improved Convergence on non-IID data", International Joint Conference on Neural Networks (IJCNN), 2024.
AbstractFederated learning (FL) has emerged as a groundbreaking paradigm in machine learning (ML), offering privacy-preserving collaborative model training across diverse datasets. Despite its promise, FL faces significant hurdles in non-identically and independently distributed (non-IID) data scenarios, where most existing methods often struggle with data heterogeneity and lack robustness in performance. This paper introduces Federated Loss Exploration ...
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