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)

Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment. - Jakob Weissteiner; Jakob Heiss; Julien Siems; Sven Seuken (IJCAI, 2022)

Bayesian Optimization-Based Combinatorial Assignment. - Jakob Weissteiner; Jakob Heiss; Julien Siems; Sven Seuken (AAAI, 2023)

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)

Machine Learning-Powered Combinatorial Clock Auction. - Ermis Nikiforos Soumalias; Jakob Weissteiner; Jakob Heiss; Sven Seuken (AAAI, 2024)

Prices, Bids, Values: Everything, Everywhere, All at Once. - Ermis Soumalias; Jakob Heiss; Jakob Weissteiner; Sven Seuken (CoRR, 2024)

Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs. - Jakob Heiss; Florian Krach; Thorsten Schmidt; Félix B. Tambe-Ndonfack (CoRR, 2024)

Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. - William Andersson; Jakob Heiss; Florian Krach; Josef Teichmann (Trans. Mach. Learn. Res., 2024)

CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk. - Ilia Azizi; Juraj Bodik; Jakob Heiss; Bin Yu (CoRR, 2025)