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ORCIDJonathan Wenger
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2020 – today
- 2025
[j1]Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig:
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty. Trans. Mach. Learn. Res. 2025 (2025)- [c7]Marvin Pförtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig:
Computation-Aware Kalman Filtering and Smoothing. AISTATS 2025: 2071-2079 - [i12]Jonathan Wenger, Beau Coker, Juraj Marusic, John P. Cunningham:
Variational Deep Learning via Implicit Regularization. CoRR abs/2505.20235 (2025) - 2024
- [c6]Kaiwen Wu, Jonathan Wenger, Haydn Thomas Jones, Geoff Pleiss, Jacob R. Gardner:
Large-Scale Gaussian Processes via Alternating Projection. AISTATS 2024: 2620-2628 - [c5]Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham:
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. NeurIPS 2024 - [i11]Marvin Pförtner, Jonathan Wenger, Jon Cockayne
, Philipp Hennig:
Computation-Aware Kalman Filtering and Smoothing. CoRR abs/2405.08971 (2024) - [i10]Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham:
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. CoRR abs/2411.01036 (2024) - 2023
- [b1]Jonathan Wenger:
Probabilistic Numerical Linear Algebra for Machine Learning. Tübingen University, Germany, 2023 - [i9]Jonathan Wenger, Felix Dangel, Agustinus Kristiadi:
On the Disconnect Between Theory and Practice of Overparametrized Neural Networks. CoRR abs/2310.00137 (2023) - [i8]Kaiwen Wu, Jonathan Wenger, Haydn Thomas Jones, Geoff Pleiss, Jacob R. Gardner:
Large-Scale Gaussian Processes via Alternating Projection. CoRR abs/2310.17137 (2023) - [i7]Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig:
Accelerating Generalized Linear Models by Trading off Computation for Uncertainty. CoRR abs/2310.20285 (2023) - 2022
- [c4]Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner:
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML 2022: 23751-23780 - [c3]Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham:
Posterior and Computational Uncertainty in Gaussian Processes. NeurIPS 2022 - [i6]Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham:
Posterior and Computational Uncertainty in Gaussian Processes. CoRR abs/2205.15449 (2022) - [i5]Marvin Pförtner, Ingo Steinwart, Philipp Hennig, Jonathan Wenger:
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. CoRR abs/2212.12474 (2022) - 2021
- [i4]Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner:
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR abs/2107.00243 (2021) - [i3]Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig:
ProbNum: Probabilistic Numerics in Python. CoRR abs/2112.02100 (2021) - 2020
- [c2]Jonathan Wenger, Hedvig Kjellström, Rudolph Triebel:
Non-Parametric Calibration for Classification. AISTATS 2020: 178-190 - [c1]Jonathan Wenger, Philipp Hennig:
Probabilistic Linear Solvers for Machine Learning. NeurIPS 2020 - [i2]Jonathan Wenger, Philipp Hennig:
Probabilistic Linear Solvers for Machine Learning. CoRR abs/2010.09691 (2020)
2010 – 2019
- 2019
- [i1]Jonathan Wenger, Hedvig Kjellström, Rudolph Triebel:
Non-Parametric Calibration for Classification. CoRR abs/1906.04933 (2019)
Coauthor Index
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