Günnemann, Stephan

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

Predicting cellular responses to complex perturbations in high‐throughput screens

Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I. L., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 307

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

Rabanser, S., Günnemann, S., & Lipton, Z. C. (2018) · arXiv

Domain Adaptation and Few-Shot LearningData Stream Mining Techniques

Cited by 204

Graph representation learning for single-cell biology

Hetzel, L., Fischer, D. S., Günnemann, S., & Theis, F. J. (2021) · Current Opinion in Systems Biology

Single-cell and spatial transcriptomicsAdvanced Graph Neural Networks

Cited by 55

Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution

Günnemann, S., Günnemann, N., & Faloutsos, C. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Anomaly Detection Techniques and ApplicationsBayesian Methods and Mixture Models

Cited by 47

Multi-Source Neural Variational Inference

Kurle, R., Günnemann, S., & Van der Smagt, P. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence

Gaussian Processes and Bayesian InferenceTopic Modeling

Cited by 22

A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness

Schwinn, L., Ladenburger, M., Beyer, T., Mofakhami, M., Gidel, G., & Günnemann, S. (2026) · arXiv

Adversarial Robustness in Machine LearningHate Speech and Cyberbullying Detection

Cited by 19

Efficient Adversarial Training in LLMs with Continuous Attacks

Xhonneux, S., Sordoni, A., Günnemann, S., Gidel, G., & Schwinn, L. (2024) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 16

Efficient Time Series Processing for Transformers and State-Space Models through Token Merging

Götz, L., Kollovieh, M., Günnemann, S., & Schwinn, L. (2024) · arXiv

Time Series Analysis and ForecastingTopic Modeling

Cited by 0