Jakob Heiss - RankMe

19 papers A* 5Journal 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks. CoRR Jakob Heiss, Sören Lambrecht, Jakob Weissteiner, Hanna Wutte, Zan Zuric, Josef Teichmann, Bin Yu
2025	J	`jnl`	CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk. CoRR Ilia Azizi, Juraj Bodik, Jakob Heiss, Bin Yu
2025	A*	`conf`	Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All. ICML Ermis Soumalias, Jakob Heiss, Jakob Weissteiner, Sven Seuken
2025	J	`jnl`	Revealing the temporal dynamics of antibiotic anomalies in the infant gut microbiome with neural jump ODEs. CoRR Anja Adamov, Markus Chardonnet, Florian Krach, Jakob Heiss, Josef Teichmann, Nicholas A. Bokulich
2024	J	`jnl`	Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. Trans. Mach. Learn. Res. William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann
2024	A*	`conf`	Machine Learning-Powered Combinatorial Clock Auction. AAAI Ermis Nikiforos Soumalias, Jakob Weissteiner, Jakob Heiss, Sven Seuken
2024	J	`jnl`	Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs. CoRR Jakob Heiss, Florian Krach, Thorsten Schmidt, Félix B. Tambe-Ndonfack
2024	J	`jnl`	Prices, Bids, Values: Everything, Everywhere, All at Once. CoRR Ermis Soumalias, Jakob Heiss, Jakob Weissteiner, Sven Seuken
2023	A*	`conf`	Bayesian Optimization-Based Combinatorial Assignment. AAAI Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
2023	J	`jnl`	Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. CoRR William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann
2023	J	`jnl`	How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function - Part II: the Multi-D Case of Two Layers with Random First Layer. CoRR Jakob Heiss, Josef Teichmann, Hanna Wutte
2023	J	`jnl`	Machine Learning-powered Combinatorial Clock Auction. CoRR Ermis Soumalias, Jakob Weissteiner, Jakob Heiss, Sven Seuken
2022	J	`jnl`	Bayesian Optimization-based Combinatorial Assignment. CoRR Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
2022	A*	`conf`	Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment. IJCAI Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
2022	A*	`conf`	NOMU: Neural Optimization-based Model Uncertainty. ICML Jakob Heiss, Jakob Weissteiner, Hanna S. Wutte, Sven Seuken, Josef Teichmann
2021	J	`jnl`	Infinite wide (finite depth) Neural Networks benefit from multi-task learning unlike shallow Gaussian Processes - an exact quantitative macroscopic characterization. CoRR Jakob Heiss, Josef Teichmann, Hanna Wutte
2021	J	`jnl`	Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment. CoRR Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
2021	J	`jnl`	NOMU: Neural Optimization-based Model Uncertainty. CoRR Jakob Heiss, Jakob Weissteiner, Hanna Wutte, Sven Seuken, Josef Teichmann
2019	J	`jnl`	How implicit regularization of Neural Networks affects the learned function - Part I. CoRR Jakob Heiss, Josef Teichmann, Hanna Wutte