publications
This page is updated periodically and may not always reflect the most recent publications. For the latest updates, please refer to my Google Scholar profile, ORCID page, or ORBi profile.
Legend: pre-print conference workshop journal miscellaneous
2025
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation 
François Rozet, Ruben Ohana, Michael McCabe, , François Lanusse, Shirley Ho.
pre-print
Simulation-Based Inference Benchmark for Weak Lensing Cosmology
Justine Zeghal, Denise Lanzieri, François Lanusse, Alexandre Boucaud, , Eric Aubourg, Adrian E Bayer, The LSST Collaboration.
Astronomy & Astrophysics
An implementation of neural simulation-based inference for parameter estimation in ATLAS
The ATLAS collaboration.
Reports on Progress in Physics
Measurement of off-shell Higgs boson production in the $H^* \to ZZ^* \to 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV pp collisions with the ATLAS detector
The ATLAS collaboration.
Reports on Progress in Physics
Appa: Bending weather dynamics with latent diffusion models for global data assimilation
Gérôme Andry, François Rozet, Sacha Lewin, Omer Rochman, Victor Mangeleer, Matthias Pirlet, Elise Faulx, Marilaure Grégoire, .
pre-print
A Neural Material Point Method for Particle-based Simulations
Omer Rochman Sharabi, Sacha Lewin, .
TMLR
2024
Low-Budget Simulation-Based Inference with Bayesian Neural Networks
Arnaud Delaunoy, Maxence de la Brassinne Bonardeaux, Siddharth Mishra-Sharma, .
pre-print
Learning Diffusion Priors from Observations by Expectation Maximization
François Rozet, Gérome Andry, François Lanusse, .
NeurIPS 2024
Video-Driven Graph Network-Based Simulators
Franciszek Szewczyk, , Matthia Sabatelli.
ML4PS workshop, NeurIPS 2024
Grasping under Uncertainties: Sequential Neural Ratio Estimation for 6-DoF Robotic Grasping
Norman Marlier, Olivier Bruls, .
IEEE Robotics and Automation Letters
Neural network-based simulation of fields and losses in electrical machines with ferromagnetic laminated cores
Florent Purnode, François Henrotte, , Christophe Geuzaine.
International Journal of Numerical Modelling
Harnessing machine learning for accurate treatment of overlapping opacity species in GCMs
Aaron David Schneider, Paul Mollière, , Ludmila Carone, Uffe Gråe Jørgensen, Leen Decin, Christiane Helling.
Astronomy & Astrophysics
Deep generative models for fast photon shower simulation in ATLAS
The ATLAS collaboration.
Computing and Software for Big Science
2023
Score-based Data Assimilation
François Rozet, .
NeurIPS 2023
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, , Alexandros Kalousis.
NeurIPS 2023
Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators
Victor Mangeleer, .
ML4PS workshop, NeurIPS 2023
Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model
François Rozet, .
ML4PS workshop, NeurIPS 2023
Trick or treat? Evaluating stability strategies in graph network-based simulators
Omer Rochman, .
ML4PS workshop, NeurIPS 2023
Dynamic NeRFs for Soccer Scenes
Sacha Lewin, Maxime Vandegar, Thomas Hoyoux, Olivier Barnich, .
6th International Workshop on Multimedia Content Analysis in Sports
Balancing Simulation-based Inference for Conservative Posteriors
Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, .
AABI 2023
Graph-informed simulation-based inference for models of active matter
Namid R Stillman, Silke Henkes, Roberto Mayor, .
ML4Materials workshop, ICLR 2023
Neural posterior estimation for exoplanetary atmospheric retrieval
Malavika Vasist, François Rozet, Olivier Absil, Paul Mollière, Evert Nasedkin, .
Astronomy & Astrophysics
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping
Norman Marlier, Julien Gustin, Olivier Brüls, .
Geometric Representations workshop, ICRA 2023
Policy Gradient Algorithms Implicitly Optimize by Continuation
Adrien Bolland, , Damien Ernst.
TMLR
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
Thibaut Théate, Antoine Wehenkel, Adrien Bolland, , and Damien Ernst.
Neurocomputing
Cracking the genetic code with neural networks
Marc Joiret, Marine Leclercq, Gaspard Lambrechts, Francesca Rapino, Pierre Close, , Liesbet Geris.
Frontiers in Artificial Intelligence
Adaptive Self-Training for Object Detection
Renaud Vandeghen, , Marc Van Droogenbroeck.
ICCV workshops 2023
2022
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, .
NeurIPS 2022
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, and .
TMLR
A deep learning approach for focal-plane wavefront sensing using vortex phase diversity
Maxime Quesnel, Gilles Orban de Xivry, , Olivier Absil.
Astronomy & Astrophysics
Simulation-based Bayesian inference for robotic grasping
Norman Marlier, Olivier Bruls, .
PRDL workshop, IROS 2022
Bayesian uncertainty quantification for machine-learned models in physics
Yarin Gal, Petros Koumoutsakos, Francois Lanusse, , Costas Papadimitriou.
Nature Reviews Physics
A simulator-based autoencoder for focal plane wavefront sensing
Maxime Quesnel, Gilles Orban de Xivry, Olivier Absil, .
SPIE Astronomical Telescopes + Instrumentation
Robust Hybrid Learning With Expert Augmentation
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, , Jörn-Henrik Jacobsen.
TMLR
A hybrid stochastic model and its Bayesian identification for infectious disease screening in a university campus with application to massive COVID-19 screening at the University of Liège
Maarten Arnst, , Romain Van Hulle, Laurent Gillet, Fabrice Bureau, Vincent Denoël.
Mathematical Biosciences
Decision-based interactive model to determine re-opening conditions of a large university campus in Belgium during the first COVID-19 wave
Vincent Denoël, Olivier Bruyère, , Fabrice Bureau, Vincent D’orio, Sébastien Fontaine, Laurent Gillet, Michèle Guillaume, Éric Haubruge, Anne-Catherine Lange, Fabienne Michel, Romain Van Hulle, Maarten Arnst, Anne-Françoise Donneau, Claude Saegerman.
Archives of Public Health
2021
HNPE: Leveraging Global Parameters for Flow-based Neural Posterior Estimation
Pedro L. C. Rodrigues, Thomas Moreau, , and Alexandre Gramfort.
NeurIPS 2021
Truncated Marginal Neural Ratio Estimation
Benjamin Miller, Alex Cole, Patrick Forré, , and Christophe Weniger.
NeurIPS 2021
From global to local MDI variable importances for random forests and when they are Shapley values
Antonio Sutera, , Van Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
NeurIPS 2021
Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference
François Rozet and .
ML4PS workshop, NeurIPS 2021
SAE: Sequential Anchored Ensembles
Arnaud Delaunoy, .
Bayesian Deep Learning workshop, NeurIPS 2021>
Towards constraining warm dark matter with stellar streams through neural simulation-based inference
Joeri Hermans, Nilanjan Banik, Christophe Weniger, Gianfranco Bertone, and .
Monthly Notices of the Royal Astronomical Society
Simulation-based Bayesian inference for multi-fingered robotic grasping
Norman Marlier, Olivier Bruls, .
pre-print
Focal Plane Wavefront Sensing using Machine Learning: Performance of Convolutional Neural Networks compared to Fundamental Limits
Gilles Orban de Xivry, Maxime Quesnel, Pierre-Olivier Vanberg, Olivier Absil, and .
Monthly Notices of the Royal Astronomical Society
Diffusion Priors In Variational Autoencoders
Antoine Wehenkel and .
INNF workshop, ICML 2021
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
Maxime Vandegar, Michael Kagan, Antoine Wehenkel, and .
AISTATS 2021
Graphical Normalizing Flows
Antoine Wehenkel and
AISTATS 2021
Toward Machine Learning Optimization of Experimental Design
Atılım Güneş Baydin et al.
Nuclear Physics News
2020
Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
Benjamin Kurt Miller, Alex Cole, , and Christophe Weniger.
ML4PS workshop, NeurIPS 2020
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
Arnaud Delaunoy et al.
ML4PS workshop, NeurIPS 2020
Improving the RSM map exoplanet detection algorithm
C.-H. Dahlqvist, , Olivier Absil
Astronomy & Astrophysics
Likelihood-free MCMC with Amortized Approximate Ratio Estimators
Joeri Hermans, Volodimir Begy, and .
ICML 2020
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms
Matthia Sabatelli, , Pierre Geurts, Marco Wiering
IJCNN 2020
You Say Normalizing Flows I see Bayesian Networks
Antoine Wehenkel and
INNF workshop, ICML 2020
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, and
PNAS
Mining gold from implicit models to improve likelihood-free inference
Johann Brehmer, , Juan Pavez, Kyle Cranmer
PNAS
2019
Unconstrained Monotonic Neural Networks
Antoine Wehenkel and
NeurIPS 2019
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atilim Gunes Baydin et al.
NeurIPS 2019
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım Güneş Baydin et al.
Supercomputing 2019
Machine learning for image-based wavefront sensing
Pierre-Olivier Vanberg, Gilles Orban de Xivry, Olivier Absil, and
ML4PS workshop, NeurIPS 2019
Mining gold: Improving simulation-based inference with latent information
Johann Brehmer, Kyle Cranmer, Siddharth Mishra-Sharma, Felix Kling, and
ML4PS workshop, NeurIPS 2019
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, , and Kyle Cranmer
The Astrophysical Journal
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
Matthia Sabatelli, , Pierre Geurts, Marco A Wiering
DRL workshop, NeurIPS 2019
Effective LHC measurements with matrix elements and machine learning
Johann Brehmer, Kyle Cranmer, Irina Espejo, Felix Kling, , Juan Pavez
Journal of Physics: Conference Series
Adversarial Variational Optimization of Non-Differentiable Simulators
, Kyle Cranmer
AISTATS 2019
2018
Likelihood-free inference with an improved cross-entropy estimator
Markus Stoye, Johann Brehmer, , Juan Pavez, Kyle Cranmer
ML4PS workshop, NeurIPS 2018
Recurrent machines for likelihood-free inference
Arthur Pesah, Antoine Wehenkel and .
2nd workshop on meta-learning, NeurIPS 2018
Robust EEG-based cross-site and cross-protocol classification of states of consciousness
Denis Engemann et al.
Brain
Deep Quality-Value (DQV) Learning
Matthia Sabatelli, , Pierre Geurts, Marco A. Wiering
BNAIC 2018
Deep generative models for fast shower simulation in ATLAS
The ATLAS collaboration
ATL-SOFT-PUB-2018-001
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson et al.
Journal of Physics: Conference Series
Gradient Energy Matching for Distributed Asynchronous Gradient Descent
Joeri Hermans,
pre-print
Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, , Juan Pavez
Physical Review Letters
A Guide to Constraining Effective Field Theories with Machine Learning
Johann Brehmer, Kyle Cranmer, , Juan Pavez
Physical Review D
Random Subspace with Trees for Feature Selection Under Memory Constraints
Antonio Sutera, Célia Chatel, , Louis Wehenkel, Pierre Geurts
AISTATS 2018
2017
Learning to Pivot with Adversarial Networks
, Michael Kagan, Kyle Cranmer
NeurIPS 2017
Neural Message Passing for Jet Physics
Isaac Henrion, Johann Brehmer, Joan Bruna, Kyunghun Cho, Kyle Cranmer, , Gaspar Rochette
Deep Learning for Physical Sciences workshop, NeurIPS 2017
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, , Kyle Cranmer, Karen Ng, Wahid Bhimji
Deep Learning for Physical Sciences workshop, NeurIPS 2017
QCD-Aware Recursive Neural Networks for Jet Physics
, Kyunghyun Cho, Cyril Becot, Kyle Cranmer
Journal of High Energy Physics
2016
Unifying generative models and exact likelihood-free inference with conditional bijections
Kyle Cranmer,
Journal of brief ideas
Experiments using machine learning to approximate likelihood ratios for mixture models
Kyle Cranmer, Juan Pavez, , WK Brooks
Journal of Physics: Conference Series
Context-dependent feature analysis with random forests
Antonio Sutera, , Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
UAI 2016
Cytomine: An open-source software for collaborative analysis of whole-slide images
Raphaël Marée, Loïc Rollus, Benjamin Stévens, Renaud Hoyoux, , Rémy Vandaele, Jean-Michel Begon, Pierre Geurts, Louis Wehenkel
European Congress on Digital Pathology
Visualization of publication impact
Eamonn Maguire, Javier Martin Montull,
EuroVis 2016
Clusterix: a visual analytics approach to clustering
Eamonn Maguire, Ilias Koutsakis, .
Symposium on Visualization in Data Science at IEEE VIS 2016
Ethnicity sensitive author disambiguation using semi-supervised learning
, Hussein Al-Natsheh, Mateusz Susik, Eamonn Maguire.
Knowledge Engineering and Semantic Web 2016
Collaborative analysis of multi-gigapixel imaging data using Cytomine
Raphaël Marée, Loïc Rollus, Benjamin Stévens, Renaud Hoyoux, , Jean-Michel Begon, Rémy Vandael, Philipp Kainz, Pierre Geurts, Louis Wehenkel.
Bioinformatics
2015
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
Kyle Cranmer, Juan Pavez, .
pre-print
Pitfalls of evaluating a classifier’s performance in high energy physics applications
, Tim Head.
pre-print
Scikit-learn: Machine Learning Without Learning the Machinery
Gael Varoquaux, Lars Buitinck, , Olivier Grisel, Fabian Pedregosa, Andreas Mueller.
GetMobile: Mobile Computing and Communications
Solar Energy Prediction: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems
Amy McGovern, David John Gagne II, Lucas Eustaquio, Gilberto Titericz Junior, Benjamin Lazorthes, Owen Zhang, , Peter Prettenhofer, Jeffrey Basara, Thomas Hamill, David Margolin.
Bulletin of the American Meteorological Society
2014
Understanding Random Forests
.
PhD thesis, University of Liège
Simple connectome inference from partial correlation statistics in calcium imaging
Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, , Damien Ernst, Pierre Geurts.
Chapter in “Neural Connectomics Challenge”
Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies
Vincent Botta, , Pierre Geurts, Louis Wehenkel.
PLOS ONE
A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning
Raphaël Marée, Loïc Rollus, Benjamin Stevens, , et al.
11th International Symposium on Biomedical Imaging
2013
Understanding variable importances in forests of randomized trees
, Louis Wehenkel, Antonio Sutera, Pierre Geurts.
NeurIPS 2013
API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck, , Mathieu Blondel, et al.
ECML/PKDD 2013
2012
Scikit-Learn: Machine Learning in Python
Fabian Pedregosa et al.
Journal of Machine Learning Research
Ensembles on Random Patches
, Pierre Geurts.
ECML/PKDD 2012
2011
Learning to rank with extremely randomized trees
Pierre Geurts, .
Learning to Rank Challenge workshop, ICML 2011
2010
A zealous parallel gradient descent algorithm
, Pierre Geurts.
Learning on Cores, Clusters and Clouds workshop, NeurIPS 2010
Collaborative filtering: Scalable approaches using restricted Boltzmann machines
.
Master thesis, University of Liège