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Antoine Wehenkel
Antoine Wehenkel
Post-doctoral Researcher
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Title
Cited by
Cited by
Year
Unconstrained Monotonic Neural Networks
A Wehenkel, G Louppe
Neural Information Processing Systems 2019 33, 2019
1742019
A deep generative model for probabilistic energy forecasting in power systems: normalizing flows
J Dumas, A Wehenkel, D Lanaspeze, B Cornélusse, A Sutera
Applied Energy 305, 117871, 2022
542022
Introducing neuromodulation in deep neural networks to learn adaptive behaviours
N Vecoven, D Ernst, A Wehenkel, G Drion
PloS one 15 (1), e0227922, 2020
442020
Parameter estimation of three-phase untransposed short transmission lines from synchrophasor measurements
A Wehenkel, A Mukhopadhyay, JY Le Boudec, M Paolone
IEEE Transactions on Instrumentation and Measurement 69 (9), 6143-6154, 2020
422020
Averting a crisis in simulation-based inference
J Hermans, A Delaunoy, F Rozet, A Wehenkel, G Louppe
Transactions on Machine Learning Research, 2021
402021
Graphical normalizing flows
A Wehenkel, G Louppe
International Conference on Artificial Intelligence and Statistics 2021, 37--45, 2020
342020
A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful
J Hermans, A Delaunoy, F Rozet, A Wehenkel, V Begy, G Louppe
Transactions on Machine Learning Research, 2022
22*2022
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
A Delaunoy, A Wehenkel, T Hinderer, S Nissanke, C Weniger, ...
Machine Learning and the Physical Sciences Workshop at NeurIPS2020, 2020
212020
Diffusion priors in variational autoencoders
A Wehenkel, G Louppe
INNF+ Workshop @ ICML2021, 2021
162021
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
M Vandegar, M Kagan, A Wehenkel, G Louppe
International Conference on Artificial Intelligence and Statistics 2021, 2020
162020
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
A Delaunoy, J Hermans, F Rozet, A Wehenkel, G Louppe
Neural Information Processing Systems 2022, 2022
132022
A probabilistic forecast-driven strategy for a risk-aware participation in the capacity firming market
J Dumas, C Cointe, A Wehenkel, A Sutera, X Fettweis, B Cornélusse
IEEE Transactions on Sustainable Energy 13 (2), 1234-1243, 2021
112021
You say Normalizing Flows I see Bayesian Networks
A Wehenkel, G Louppe
INNF+ Workshop @ ICML2020, 2020
102020
An app-based algorithmic approach for harvesting local and renewable energy using electric vehicles
A Dubois*, A Wehenkel*, R Fonteneau, F Olivier, D Ernst
Proceedings of the 9th International Conference on Agents and Artificial …, 2017
102017
Recurrent machines for likelihood-free inference
A Pesah*, A Wehenkel*, G Louppe
MetaLearn Workshop @ NeurIPS2018, 2018
72018
Robust Hybrid Learning With Expert Augmentation
A Wehenkel, J Behrmann, H Hsu, G Sapiro, G Louppe, JH Jacobsen
Transactions on Machine Learning Research, 2022
5*2022
Distributional reinforcement learning with unconstrained monotonic neural networks
T Théate, A Wehenkel, A Bolland, G Louppe, D Ernst
Neurocomputing 534, 199-219, 2023
32023
A probabilistic forecast-driven strategy for a risk-aware participation in the capacity firming market: extended version
J Dumas, C Cointe, A Wehenkel, A Sutera, X Fettweis, B Cornélusse
arXiv preprint arXiv:2105.13801, 2021
12021
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
M Falkiewicz, N Takeishi, I Shekhzadeh, A Wehenkel, A Delaunoy, ...
Advances in Neural Information Processing Systems 36, 2024
2024
Inferring Cardiovascular Biomarkers with Hybrid Model Learning
O Senouf, J Behrmann, JH Jacobsen, P Frossard, E Abbe, A Wehenkel
NeurIPS 2023 Workshop on Deep Learning and Inverse Problems, 2023
2023
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