An App-based Algorithmic Approach for Harvesting Local and Renewable Energy using Electric Vehicles. - Antoine Dubois; Antoine Wehenkel; Raphael Fonteneau; Frédéric Olivier; Damien Ernst (ICAART (1), 2017)

Recurrent machines for likelihood-free inference. - Arthur Pesah; Antoine Wehenkel; Gilles Louppe (CoRR, 2018)

Unconstrained Monotonic Neural Networks. - Antoine Wehenkel; Gilles Louppe (NeurIPS, 2019)

You say Normalizing Flows I see Bayesian Networks. - Antoine Wehenkel; Gilles Louppe (CoRR, 2020)

Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization. - Arnaud Delaunoy; Antoine Wehenkel; Tanja Hinderer; Samaya Nissanke; Christoph Weniger; Andrew R. Williamson; Gilles Louppe (CoRR, 2020)

Parameter Estimation of Three-Phase Untransposed Short Transmission Lines From Synchrophasor Measurements. - Antoine Wehenkel; Arpan Mukhopadhyay; Jean-Yves Le Boudec; Mario Paolone (IEEE Trans. Instrum. Meas., 2020)

Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. - Maxime Vandegar; Michael Kagan; Antoine Wehenkel; Gilles Louppe (AISTATS, 2021)

Graphical Normalizing Flows. - Antoine Wehenkel; Gilles Louppe (AISTATS, 2021)

A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market. - Jonathan Dumas; Colin Cointe; Antoine Wehenkel; Antonio Sutera; Xavier Fettweis; Bertrand Cornélusse (CoRR, 2021)

Deep generative modeling for probabilistic forecasting in power systems. - Jonathan Dumas; Antoine Wehenkel; Damien Lanaspeze; Bertrand Cornélusse; Antonio Sutera (CoRR, 2021)

Diffusion Priors In Variational Autoencoders. - Antoine Wehenkel; Gilles Louppe (CoRR, 2021)

Averting A Crisis In Simulation-Based Inference. - Joeri Hermans; Arnaud Delaunoy; François Rozet; Antoine Wehenkel; Gilles Louppe (CoRR, 2021)

Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation. - Arnaud Delaunoy; Joeri Hermans; François Rozet; Antoine Wehenkel; Gilles Louppe (NeurIPS, 2022)

A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful. - Joeri Hermans; Arnaud Delaunoy; François Rozet; Antoine Wehenkel; Volodimir Begy; Gilles Louppe (Trans. Mach. Learn. Res., 2022)

Inductive Bias In Deep Probabilistic Modelling. - Antoine Wehenkel (2022)

Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability. - Maciej Falkiewicz; Naoya Takeishi; Imahn Shekhzadeh; Antoine Wehenkel; Arnaud Delaunoy; Gilles Louppe; Alexandros Kalousis (NeurIPS, 2023)

Simulation-based Inference for Cardiovascular Models. - Antoine Wehenkel; Jens Behrmann; Andrew C. Miller; Guillermo Sapiro; Ozan Sener; Marco Cuturi; Jörn-Henrik Jacobsen (CoRR, 2023)

Distributional reinforcement learning with unconstrained monotonic neural networks. - Thibaut Théate; Antoine Wehenkel; Adrien Bolland; Gilles Louppe; Damien Ernst (Neurocomputing, 2023)

Robust Hybrid Learning With Expert Augmentation. - Antoine Wehenkel; Jens Behrmann; Hsiang Hsu; Guillermo Sapiro; Gilles Louppe; Jörn-Henrik Jacobsen (Trans. Mach. Learn. Res., 2023)

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration. - Antoine Wehenkel; Juan L. Gamella; Ozan Sener; Jens Behrmann; Guillermo Sapiro; Marco Cuturi; Jörn-Henrik Jacobsen (CoRR, 2024)

Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers. - Laura Manduchi; Antoine Wehenkel; Jens Behrmann; Luca Pegolotti; Andy C. Miller; Ozan Sener; Marco Cuturi; Guillermo Sapiro; Jörn-Henrik Jacobsen (CoRR, 2024)