About

I started as a PhD student at the Statistical Learning & Data Science working group in February 2022.

I obtained a Bachelor's degree (B.A.) in Business Administration from DHBW Ravensburg and a Bachelor's and consecutive Master's degree (B.Sc., M.Sc.) in Statistics from LMU Munich.

My main research focus is on the quantification of uncertainty, where I am active in our research subgroups Probabilistic Machine and Deep Learning and Causal and Fair Machine Learning.

Furthermore, I am part of the Data Science Group chaired by Prof. David Rügamer.

I receive funding from the Konrad Zuse School of Excellence in Reliable AI (relAI).

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

lisa.wimmer [at] stat.uni-muenchen.de

Research interests

• Predictive uncertainty
• Probabilistic machine learning
• Bayesian deep learning
• Uncertainty and causal inference
• Imprecise probabilities
• Weak supervision

You can find me on

• Google Scholar
• GitHub
• LinkedIn

References

1. Vahidi A, Wimmer L, Gündüz HA, Bischl B, Hüllermeier E, Rezaei M (2024) Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
   link|pdf.
2. Sale Y, Hofman P, Löhr T, Wimmer L, Nagler T, Hüllermeier E (2024) Label-wise Aleatoric and Epistemic Uncertainty Quantification 40th Conference on Uncertainty in Artificial Intelligence (UAI),
   link|pdf.
3. Sommer E, Wimmer L, Papamarkou T, Bothmann L, Bischl B, Rügamer D (2024) Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? 41st International Conference on Machine Learning (ICML),
   link|pdf.
4. Wiese JG, Wimmer L, Papamarkou T, Bischl B, Günnemann S, Rügamer D (2024) Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract) 33rd International Joint Conferences on Artificial Intelligence (IJCAI),
   link|pdf.
5. Vahidi A, Schosser S, Wimmer L, Li Y, Bischl B, Hüllermeier E, Rezaei M (2024) ProSMIN: Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization 12th International Conference on Learning Representations (ICLR),
   link|pdf.
6. Wiese JG, Wimmer L, Papamarkou T, Bischl B, Günnemann S, Rügamer D (2023) Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
   link|pdf.
7. Bothmann L, Wimmer L, Charrakh O, Weber T, Edelhoff H, Peters W, Nguyen H, Benjamin C, Menzel A (2023) Automated wildlife image classification: An active learning tool for ecological applications. Ecological Informatics 77.
   link|pdf.
8. Wimmer L, Sale Y, Hofman P, Bischl B, Hüllermeier E (2023) Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? 39th Conference on Uncertainty in Artificial Intelligence (UAI),
   link|pdf.