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ORCIDMathias Kraus
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- affiliation: University of Erlangen-Nuremberg, Nuremberg, Germany
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2020 – today
- 2024
[j11]Kristof Coussement, Mohammad Zoynul Abedin
, Mathias Kraus, Sebastián Maldonado, Kazim Topuz:
Explainable AI for enhanced decision-making. Decis. Support Syst. 184: 114276 (2024)- [j10]Koen W. De Bock, Kristof Coussement, Arno De Caigny, Roman Slowinski, Bart Baesens, Robert N. Boute, Tsan-Ming Choi, Dursun Delen, Mathias Kraus, Stefan Lessmann, Sebastián Maldonado, David Martens, María Óskarsdóttir, Carla Vairetti, Wouter Verbeke, Richard Weber:
Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. Eur. J. Oper. Res. 317(2): 249-272 (2024) - [j9]Mathias Kraus, Daniel Tschernutter, Sven Weinzierl, Patrick Zschech:
Interpretable generalized additive neural networks. Eur. J. Oper. Res. 317(2): 303-316 (2024) - [j8]Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky:
Data-Driven Allocation of Preventive Care with Application to Diabetes Mellitus Type II. Manuf. Serv. Oper. Manag. 26(1): 137-153 (2024) - [j7]Daniel Tschernutter, Mathias Kraus, Stefan Feuerriegel:
A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions. Trans. Mach. Learn. Res. 2024 (2024) - [c17]Tobias Schimanski, Jingwei Ni, Mathias Kraus, Elliott Ash, Markus Leippold:
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering. ACL (1) 2024: 1913-1931 - [c16]Mathias Kraus, Nico Hambauer, Kristina Müller, Pavlina Kröckel, Nalika Ulapane, Arno De Caigny, Koen W. De Bock, Kristof Coussement:
Coupling Neural Networks Between Clusters for Better Personalized Care. HICSS 2024: 3627-3636 - [i24]Daniel Tschernutter, Mathias Kraus, Stefan Feuerriegel:
A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions. CoRR abs/2401.07936 (2024) - [i23]Tobias Schimanski, Jingwei Ni, Mathias Kraus, Elliott Ash, Markus Leippold:
Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering. CoRR abs/2402.08277 (2024) - [i22]Theodor Stoecker, Nico Hambauer, Patrick Zschech, Mathias Kraus:
IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight. CoRR abs/2403.11363 (2024) - [i21]Sandra Zilker, Sven Weinzierl, Mathias Kraus, Patrick Zschech, Martin Matzner:
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis. CoRR abs/2405.13187 (2024) - [i20]Sven Kruschel, Nico Hambauer, Sven Weinzierl, Sandra Zilker, Mathias Kraus, Patrick Zschech:
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models. CoRR abs/2409.14429 (2024) - [i19]Sven Kruschel, Lasse Bohlen, Julian Rosenberger, Patrick Zschech, Mathias Kraus:
Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability. CoRR abs/2409.16870 (2024) - 2023
- [j6]Justus Zipfel, Felix Verworner, Marco Fischer, Uwe Wieland, Mathias Kraus, Patrick Zschech:
Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models. Comput. Ind. Eng. 177: 109045 (2023) - [c15]Dominik Stammbach, Nicolas Webersinke, Julia Anna Bingler, Mathias Kraus, Markus Leippold:
Environmental Claim Detection. ACL (2) 2023: 1051-1066 - [c14]Nilmini Wickramasinghe, Nalika Ulapane, Amirhossein Eslami Andargoli, Jan Miltner, Jule van de Logt, Mathias Kraus, Freimut Bodendorf:
A Suggested Blockchain Architecture for Healthcare Data Sharing. AMCIS 2023 - [c13]Sandra Zilker, Sven Weinzierl, Patrick Zschech, Mathias Kraus, Martin Matzner:
Best of Both Worlds: Combining Predictive Power with Interpretable and Explainable Results for Patient Pathway Prediction. ECIS 2023 - [c12]Jingwei Ni, Julia Anna Bingler, Chiara Colesanti Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold:
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools. EMNLP (Demos) 2023: 21-51 - [c11]Tobias Schimanski, Julia Anna Bingler, Mathias Kraus, Camilla Hyslop, Markus Leippold:
ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets. EMNLP 2023: 15745-15756 - [i18]Mathias Kraus, Julia Anna Bingler, Markus Leippold, Tobias Schimanski, Chiara Colesanti Senni, Dominik Stammbach, Saeid Ashraf Vaghefi, Nicolas Webersinke:
Enhancing Large Language Models with Climate Resources. CoRR abs/2304.00116 (2023) - [i17]Saeid Ashraf Vaghefi, Qian Wang, Veruska Muccione, Jingwei Ni, Mathias Kraus, Julia Anna Bingler, Tobias Schimanski, Chiara Colesanti Senni, Nicolas Webersinke, Christian Huggel, Markus Leippold:
chatClimate: Grounding Conversational AI in Climate Science. CoRR abs/2304.05510 (2023) - [i16]Jingwei Ni, Julia Anna Bingler, Chiara Colesanti Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold:
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools. CoRR abs/2307.15770 (2023) - [i15]Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky:
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II. CoRR abs/2308.06959 (2023) - [i14]Tobias Schimanski, Julia Anna Bingler, Camilla Hyslop, Mathias Kraus, Markus Leippold:
ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targets. CoRR abs/2310.08096 (2023) - 2022
- [c10]Patrick Zschech, Sven Weinzierl, Nico Hambauer, Sandra Zilker, Mathias Kraus:
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. ECIS 2022 - [c9]Daniel Hershcovich, Nicolas Webersinke, Mathias Kraus, Julia Anna Bingler, Markus Leippold:
Towards Climate Awareness in NLP Research. EMNLP 2022: 2480-2494 - [c8]Maximilian V. Harl, Marvin Herchenbach, Sven Kruschel, Nico Hambauer, Patrick Zschech, Mathias Kraus:
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision. Wirtschaftsinformatik 2022 - [i13]Maximilian Harl, Marvin Herchenbach, Sven Kruschel, Nico Hambauer, Patrick Zschech, Mathias Kraus:
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision. CoRR abs/2201.02028 (2022) - [i12]Patrick Zschech, Sven Weinzierl, Nico Hambauer, Sandra Zilker, Mathias Kraus:
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. CoRR abs/2204.09123 (2022) - [i11]Daniel Hershcovich, Nicolas Webersinke, Mathias Kraus, Julia Anna Bingler, Markus Leippold:
Towards Climate Awareness in NLP Research. CoRR abs/2205.05071 (2022) - [i10]Dominik Stammbach, Nicolas Webersinke, Julia Anna Bingler, Mathias Kraus, Markus Leippold:
A Dataset for Detecting Real-World Environmental Claims. CoRR abs/2209.00507 (2022) - 2021
- [c7]Basil Maag, Stefan Feuerriegel, Mathias Kraus, Maytal Saar-Tsechansky, Thomas Züger:
Modeling longitudinal dynamics of comorbidities. CHIL 2021: 222-235 - [c6]Yilmazcan Özyurt, Mathias Kraus, Tobias Hatt, Stefan Feuerriegel:
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units. KDD 2021: 3452-3462 - [i9]Yilmazcan Özyurt, Mathias Kraus, Tobias Hatt, Stefan Feuerriegel:
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units. CoRR abs/2102.04702 (2021) - [i8]Nicolas Webersinke, Mathias Kraus, Julia Anna Bingler, Markus Leippold:
ClimateBert: A Pretrained Language Model for Climate-Related Text. CoRR abs/2110.12010 (2021) - 2020
- [b1]Mathias Kraus:
Deep Learning in Business Analytics: Methods and Applications. ETH Zurich, Zürich, Switzerland, 2020 - [j5]Mathias Kraus, Stefan Feuerriegel, Asil Oztekin:
Deep learning in business analytics and operations research: Models, applications and managerial implications. Eur. J. Oper. Res. 281(3): 628-641 (2020) - [c5]Martin Maritsch, Simon Föll, Vera Lehmann, Caterina Bérubé, Mathias Kraus, Stefan Feuerriegel, Tobias Kowatsch, Thomas Züger, Christoph Stettler, Elgar Fleisch, Felix Wortmann:
Towards Wearable-based Hypoglycemia Detection and Warning in Diabetes. CHI Extended Abstracts 2020: 1-8 - [c4]Francesco Ducci, Mathias Kraus, Stefan Feuerriegel:
Cascade-LSTM: A Tree-Structured Neural Classifier for Detecting Misinformation Cascades. KDD 2020: 2666-2676
2010 – 2019
- 2019
- [j4]Mathias Kraus, Stefan Feuerriegel:
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences. Decis. Support Syst. 125 (2019) - [j3]Mathias Kraus, Stefan Feuerriegel:
Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees. Expert Syst. Appl. 118: 65-79 (2019) - [c3]Martin Maritsch, Caterina Bérubé, Mathias Kraus, Vera Lehmann, Thomas Züger, Stefan Feuerriegel, Tobias Kowatsch, Felix Wortmann:
Improving heart rate variability measurements from consumer smartwatches with machine learning. UbiComp/ISWC Adjunct 2019: 934-938 - [c2]Hergen Wolf, Rafael Lorenz, Mathias Kraus, Stefan Feuerriegel, Torbjørn H. Netland:
Bringing Advanced Analytics to Manufacturing: A Systematic Mapping. APMS (1) 2019: 333-340 - [c1]Mathias Kraus, Stefan Feuerriegel:
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching. KDD 2019: 2643-2652 - [i7]Mathias Kraus, Stefan Feuerriegel:
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching. CoRR abs/1905.13131 (2019) - [i6]Mathias Kraus, Stefan Feuerriegel:
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences. CoRR abs/1907.05146 (2019) - [i5]Martin Maritsch, Caterina Bérubé, Mathias Kraus, Vera Lehmann, Thomas Züger, Stefan Feuerriegel, Tobias Kowatsch, Felix Wortmann:
Improving Heart Rate Variability Measurements from Consumer Smartwatches with Machine Learning. CoRR abs/1907.07496 (2019) - 2018
- [j2]Bernhard Kratzwald, Suzana Ilic, Mathias Kraus, Stefan Feuerriegel, Helmut Prendinger:
Deep learning for affective computing: Text-based emotion recognition in decision support. Decis. Support Syst. 115: 24-35 (2018) - [i4]Bernhard Kratzwald, Suzana Ilic, Mathias Kraus, Stefan Feuerriegel, Helmut Prendinger:
Decision support with text-based emotion recognition: Deep learning for affective computing. CoRR abs/1803.06397 (2018) - [i3]Mathias Kraus, Stefan Feuerriegel, Asil Oztekin:
Deep learning in business analytics and operations research: Models, applications and managerial implications. CoRR abs/1806.10897 (2018) - 2017
- [j1]Mathias Kraus, Stefan Feuerriegel:
Decision support from financial disclosures with deep neural networks and transfer learning. Decis. Support Syst. 104: 38-48 (2017) - [i2]Mathias Kraus, Stefan Feuerriegel:
Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees. CoRR abs/1704.05228 (2017) - [i1]Mathias Kraus, Stefan Feuerriegel:
Decision support from financial disclosures with deep neural networks and transfer learning. CoRR abs/1710.03954 (2017)
Coauthor Index
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last updated on 2024-10-18 20:32 CEST by the dblp team
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