Apple
Zürich, Switzerland
+32 497 06 91 46
+41 76 722 83 75

firstname.name@gmail.com

Antoine Wehenkel

Postdoctoral Researcher

About me

Since November 2022, I have been a postdoctoral researcher in Machine Learning at Apple under the advisory of Jörn-Henrik Jacobsen (Health AI) and Marco Cuturi (ML Research). I explore strategies to derive robust simulation-based inference algorithms for misspecified simulators and their applications to health technologies. Before joining Apple, I obtained my PhD (FNRS Research Fellowship) in computer science under the supervision of Professor Gilles Louppe (Uliège - Belgium) in October 2022.

During the summer 2021, I interned at Amazon Web Services where I worked on automated code analysis. In 2018, I was graduated in computer engineering (M. Sc.) from ULiège. I spent my last year of study at Ecole Polytechnique Fédérale de Lausanne (EPFL) as an exchange student. There, I did my master's thesis in Jean-Yves Le Boudec's laboratory, which was about the parameters estimation of electrical distribution networks' lines.

In my dreams, I want to boost the interaction between fundamental sciences and machine learning methods with the objective of making scientific discovery but also for building predictive models that can be deployed more reliably in the real world. When I wake up, I try to advance simulation-based inference by exploring new means for implementing more effectively inductive bias into deep generative models. My main research interests are in deep probabilistic modeling, causal models and simulation-based inference.

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Publications

In Preparation

Peer Reviewed

2022
  • Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation

    Neural Information Processing Systems 2022
  • Averting a crisis in simulation-based inference

    Transactions on Machine Learning Research
  • A deep generative model for probabilistic energy forecasting in power systems: normalizing flows

    Applied Energy
  • 2021
    2020
    2019 and before...

    Presentations

    And posters

    Inductive Bias in Deep Probabilistic Modelling

    PhD Dissertation - University of Liège
    November 2022
    Link to the slides

    The Symbiosis between Deep Probabilistic and Scientific Models

    Presentation - HES Geneva
    October 2022
    Link to the slides

    The Symbiosis between Deep Probabilistic and Scientific Models

    Presentation - Gen U 2022, Copenhagen
    September 2022
    Link to the slides

    Normalizing Flows and Bayesian networks

    Presentation - HES Geneva
    February 2022
    Link to the slides

    Normalizing Flows and Bayesian networks

    Presentation - CogSys seminar, DTU
    October 2020
    Link to the slides Link to the video

    Normalizing Flows for Probabilistic Modeling and Inference

    Presentation - ML Journal Club, ULiège
    April 2020
    Link to the slides

    Unconstrained Monotonic Neural Networks

    Poster - NeurIPS 2019 @ Vancouver
    December 2019
    Link to the poster Link to the paper

    Neural Likelihood-Free Inference

    Presentation - Grappa, UvA in Amsterdam
    November 2019
    Link to the slides

    Unconstrained Monotonic Neural Networks

    Presentation - Benelearn 2019 @ Brussels
    November 2019
    Link to the slides

    Unconstrained Monotonic Neural Networks

    Poster - Prairie AI Summer School 2019 @ Paris
    October 2019
    Link to the poster

    Recurrent Machines For Likelihood Free Inference

    Poster - MetaLearn Workshop @ NeurIPS 2018, Montreal
    December 2018
    Link to the poster