Hüllermeier, Eyke

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences

Artificial Intelligence in EducationArtificial Intelligence in Healthcare and Education

Cited by 7,552

ChatGPT for good? On opportunities and challenges of large language models for education

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences

Artificial Intelligence in EducationArtificial Intelligence in Healthcare and Education

Cited by 7,326

Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Hüllermeier, E., & Waegeman, W. (2021) · Machine Learning

Adversarial Robustness in Machine LearningGaussian Processes and Bayesian Inference

Cited by 1,834

Epistemic Uncertainty Sampling

Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019) · Lecture Notes in Computer Science

Machine Learning and AlgorithmsDomain Adaptation and Few-Shot Learning

Cited by 48

A Survey of Reinforcement Learning from Human Feedback

Kaufmann, T., Weng, P., Bengs, V., & Hüllermeier, E. (2023) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 43

Pitfalls of Epistemic Uncertainty Quantification Through Loss Minimisation

Bengs, V., Hüllermeier, E., & Waegeman, W. (2022) · Advances in Neural Information Processing Systems 35

Probabilistic and Robust Engineering DesignGaussian Processes and Bayesian Inference

Cited by 15

Ensemble-based Uncertainty Quantification: Bayesian versus Credal Inference

Shaker, M., & Hüllermeier, E. (2021) · Proceedings - 31. Workshop Computational Intelligence : Berlin, 25. - 26. November 2021

Adversarial Robustness in Machine LearningBayesian Modeling and Causal Inference

Cited by 9