How implicit regularization of Neural Networks affects the learned function - Part I. - Jakob Heiss; Josef Teichmann; Hanna Wutte (CoRR, 2019)

Infinite wide (finite depth) Neural Networks benefit from multi-task learning unlike shallow Gaussian Processes - an exact quantitative macroscopic characterization. - Jakob Heiss; Josef Teichmann; Hanna Wutte (CoRR, 2021)

NOMU: Neural Optimization-based Model Uncertainty. - Jakob Heiss; Jakob Weissteiner; Hanna S. Wutte; Sven Seuken; Josef Teichmann (ICML, 2022)

How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function - Part II: the Multi-D Case of Two Layers with Random First Layer. - Jakob Heiss; Josef Teichmann; Hanna Wutte (CoRR, 2023)

Robust Utility Optimization via a GAN Approach. - Florian Krach; Josef Teichmann; Hanna Wutte (CoRR, 2024)