Antoine Wehenkel

About Antoine Wehenkel

Antoine Wehenkel, With an exceptional h-index of 11 and a recent h-index of 11 (since 2020), a distinguished researcher at Université de Liège, specializes in the field of Deep Probabilistic Modeling, Scientific Modeling, Health AI.

His recent articles reflect a diverse array of research interests and contributions to the field:

Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability

Inferring Cardiovascular Biomarkers with Hybrid Model Learning

Simulation-based Inference for Cardiovascular Models

Distributional reinforcement learning with unconstrained monotonic neural networks

A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful

Inductive Bias In Deep Probabilistic Modelling

Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation

Robust Hybrid Learning With Expert Augmentation

Antoine Wehenkel Information

University

Position

Phd student (FNRS)

Citations(all)

498

Citations(since 2020)

493

Cited By

64

hIndex(all)

11

hIndex(since 2020)

11

i10Index(all)

13

i10Index(since 2020)

13

Email

University Profile Page

Google Scholar

Antoine Wehenkel Skills & Research Interests

Deep Probabilistic Modeling

Scientific Modeling

Health AI

Top articles of Antoine Wehenkel

Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability

Advances in Neural Information Processing Systems

2024/2/13

Antoine Wehenkel
Antoine Wehenkel

H-Index: 4

Inferring Cardiovascular Biomarkers with Hybrid Model Learning

2023/11/3

Simulation-based Inference for Cardiovascular Models

arXiv preprint arXiv:2307.13918

2023/7/26

Antoine Wehenkel
Antoine Wehenkel

H-Index: 4

Guillermo Sapiro
Guillermo Sapiro

H-Index: 69

Distributional reinforcement learning with unconstrained monotonic neural networks

Neurocomputing

2023/5/14

Antoine Wehenkel
Antoine Wehenkel

H-Index: 4

Damien Ernst
Damien Ernst

H-Index: 31

A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful

Transactions on Machine Learning Research

2022/12

Inductive Bias In Deep Probabilistic Modelling

2022/10/26

Antoine Wehenkel
Antoine Wehenkel

H-Index: 4

Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation

Neural Information Processing Systems 2022

2022/8/29

Joeri Hermans
Joeri Hermans

H-Index: 6

Antoine Wehenkel
Antoine Wehenkel

H-Index: 4

Robust Hybrid Learning With Expert Augmentation

arXiv preprint arXiv:2202.03881

2022/2/8

A deep generative model for probabilistic energy forecasting in power systems: normalizing flows

Applied Energy

2022/1/1

A probabilistic forecast-driven strategy for a risk-aware participation in the capacity firming market

IEEE Transactions on Sustainable Energy

2021/10/6

Diffusion priors in variational autoencoders

INNF+ Workshop @ ICML2021

2021/6/29

Antoine Wehenkel
Antoine Wehenkel

H-Index: 4

Introducing neuromodulation in deep neural networks to learn adaptive behaviours

PloS one

2020/1/27

Parameter estimation of three-phase untransposed short transmission lines from synchrophasor measurements

IEEE Transactions on Instrumentation and Measurement

2020/1/23

Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization

arXiv preprint arXiv:2010.12931

2020/10/24

Antoine Wehenkel
Antoine Wehenkel

H-Index: 4

Christoph Weniger
Christoph Weniger

H-Index: 33

You say Normalizing Flows I see Bayesian Networks

2020/6/1

Antoine Wehenkel
Antoine Wehenkel

H-Index: 4

Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference

2020/4

Graphical normalizing flows

2020/4

Antoine Wehenkel
Antoine Wehenkel

H-Index: 4

See List of Professors in Antoine Wehenkel University(Université de Liège)

Co-Authors

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