Stefan Feuerriegel
ETH Zurich
Financial economicsDecision support systemMachine learningEnvironmental economicsBusinessEconometricsArtificial intelligenceEconomicsInformation systemOrder (exchange)Financial marketNatural language processingElectricityDemand responseStock marketComputer scienceArtificial neural networkSentiment analysisInformation processingReinforcement learning
184Publications
23H-index
1,411Citations
Publications 156
Newest
#1Nicolas Pröllochs (University of Giessen)H-Index: 7
#2Dominik Bär (LMU: Ludwig Maximilian University of Munich)H-Index: 1
Last. Stefan Feuerriegel (LMU: Ludwig Maximilian University of Munich)H-Index: 23
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Emotions are regarded as a dominant driver of human behavior, and yet their role in online rumor diffusion is largely unexplored. In this study, we empirically study the extent to which emotions explain the diffusion of online rumors. We analyze a large-scale sample of 107,014 online rumors from Twitter, as well as their cascades. For each rumor, the embedded emotions were measured based on eight so-called basic emotions from Plutchik’s wheel of emotions (i.e., anticipation–surprise, anger–fear,...
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#1Johannes Jakubik (ETH Zurich)H-Index: 1
#2Adrian Binding (ETH Zurich)H-Index: 1
Last. Stefan Feuerriegel (ETH Zurich)H-Index: 23
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Abstract Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with randomized step lengths. PSO frequently accelerates optimization in practical applications, where gradients are not available and function evaluations expensive. Yet the traditional PSO algorithm ignores the potential knowledge that could have been gained of the objective function from the observations by indi...
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#1Mateusz Dolata (UZH: University of Zurich)H-Index: 9
#2Stefan Feuerriegel (LMU: Ludwig Maximilian University of Munich)H-Index: 23
Last. Gerhard Schwabe (UZH: University of Zurich)H-Index: 3
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Algorithmic fairness (AF) has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept and that technologies for AF should therefore be approached through a sociotechnical lens. We advance the discourse on AF as a sociotechnical phenomenon. Our research objecti...
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#1Arne Seeliger (ETH Zurich)H-Index: 1
#2Gerrit Merz (KIT: Karlsruhe Institute of Technology)
Last. Stefan Feuerriegel (ETH Zurich)H-Index: 23
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In this paper, we present an analysis of eye gaze patterns pertaining to visual cues in augmented reality (AR) for head-mounted displays (HMDs). We conducted an experimental study involving a picking and assembly task, which was guided by different visual cues. We compare these visual cues along multiple dimensions (in-view vs. out-of-view, static vs. dynamic, sequential vs. simultaneous) and analyze quantitative metrics such as gaze distribution, gaze duration, and gaze path distance. Our resul...
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Acute generalized exanthematous pustulosis (AGEP) is a rare skin adverse drug reaction. The pathophysiology and causative drugs associated with AGEP are poorly understood, with the majority of studies in AGEP focusing on a single-drug-outcome association. We therefore aimed to explore and characterize frequently reported drug combinations associated with AGEP using the WHO pharmacovigilance database VigiBase. In this explorative cross-sectional study of a pharmacovigilance database using a data-...
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#1Moritz von ZahnH-Index: 1
#2Stefan Feuerriegel (ETH Zurich)H-Index: 23
Last. Niklas Kuehl (KIT: Karlsruhe Institute of Technology)H-Index: 3
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Aug 31, 2021 in EMNLP (Empirical Methods in Natural Language Processing)
#1Zhenrui Yue (TUM: Technische Universität München)
#2Bernhard Kratzwald (ETH Zurich)H-Index: 8
Last. Stefan Feuerriegel (ETH Zurich)H-Index: 23
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Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains. These approaches circumvent the need for manually annotated training data from the new domain and, instead, generate synthetic question-answer pairs that are used for training. However, existing methods for question generation rely on large amounts of synthetically generated datasets and costly computational resources, which render these techniques widely inaccessible when the...
#1Kerstin Noëlle Vokinger (Brigham and Women's Hospital)H-Index: 9
#2Stefan Feuerriegel (LMU: Ludwig Maximilian University of Munich)H-Index: 23
Last. Aaron S. Kesselheim (Brigham and Women's Hospital)H-Index: 62
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Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias across the different development steps of machine learning-based systems for medical applications. Vokinger et al. discuss potential sources of bias in machine learning systems used in medicine. The authors propose solutions to mitigate bias across the different stages of model development, from data collection and prepa...
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Aug 14, 2021 in KDD (Knowledge Discovery and Data Mining)
#1Amray Schwabe (ETH Zurich)H-Index: 1
#2Joel Persson (ETH Zurich)H-Index: 3
Last. Stefan Feuerriegel (ETH Zurich)H-Index: 23
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To manage the COVID-19 epidemic effectively, decision-makers in public health need accurate forecasts of case numbers. A potential near real-time predictor of future case numbers is human mobility; however, research on the predictive power of mobility is lacking. To fill this gap, we introduce a novel model for epidemic forecasting based on mobility data, called mobility marked Hawkes model. The proposed model consists of three components: (1) A Hawkes process captures the transmission dynamics ...
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#1Stefan Feuerriegel (NII: National Institute of Informatics)H-Index: 23
#2Ruben Geraldes (NII: National Institute of Informatics)H-Index: 4
Last. Helmut Prendinger (NII: National Institute of Informatics)H-Index: 40
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