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Jakob Heiss
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2020 – today
- 2024
- [j1]William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann:
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. Trans. Mach. Learn. Res. 2024 (2024) - [c4]Ermis Nikiforos Soumalias, Jakob Weissteiner, Jakob Heiss
, Sven Seuken:
Machine Learning-Powered Combinatorial Clock Auction. AAAI 2024: 9891-9900 - 2023
- [c3]Jakob Weissteiner, Jakob Heiss
, Julien Siems, Sven Seuken:
Bayesian Optimization-Based Combinatorial Assignment. AAAI 2023: 5858-5866 - [i8]Jakob Heiss
, Josef Teichmann, Hanna Wutte:
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 abs/2303.11454 (2023) - [i7]William Andersson, Jakob Heiss
, Florian Krach, Josef Teichmann:
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. CoRR abs/2307.13147 (2023) - [i6]Ermis Soumalias, Jakob Weissteiner, Jakob Heiss
, Sven Seuken:
Machine Learning-powered Combinatorial Clock Auction. CoRR abs/2308.10226 (2023) - 2022
- [c2]Jakob Heiss
, Jakob Weissteiner, Hanna S. Wutte, Sven Seuken, Josef Teichmann:
NOMU: Neural Optimization-based Model Uncertainty. ICML 2022: 8708-8758 - [c1]Jakob Weissteiner, Jakob Heiss
, Julien Siems, Sven Seuken:
Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment. IJCAI 2022: 541-548 - [i5]Jakob Weissteiner, Jakob Heiss
, Julien Siems, Sven Seuken:
Bayesian Optimization-based Combinatorial Assignment. CoRR abs/2208.14698 (2022) - 2021
- [i4]Jakob Heiss
, Jakob Weissteiner, Hanna Wutte, Sven Seuken, Josef Teichmann:
NOMU: Neural Optimization-based Model Uncertainty. CoRR abs/2102.13640 (2021) - [i3]Jakob Weissteiner, Jakob Heiss
, Julien Siems, Sven Seuken:
Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment. CoRR abs/2109.15117 (2021) - [i2]Jakob Heiss
, Josef Teichmann, Hanna Wutte:
Infinite wide (finite depth) Neural Networks benefit from multi-task learning unlike shallow Gaussian Processes - an exact quantitative macroscopic characterization. CoRR abs/2112.15577 (2021)
2010 – 2019
- 2019
- [i1]Jakob Heiss
, Josef Teichmann, Hanna Wutte:
How implicit regularization of Neural Networks affects the learned function - Part I. CoRR abs/1911.02903 (2019)
Coauthor Index
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last updated on 2024-08-10 01:21 CEST by the dblp team
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