63.6k citations · 68k across the 67 of their papers we have counts for
68 papers
An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning
Maximilian Dax, Theo Heimel, Gilles Louppe
Simulation-based inference (SBI) with machine learning is an increasingly important tool for solving inverse problems in science and engineering, including parameter inference and…
Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation
Gérôme Andry, Sacha Lewin, François Rozet +6
Deep learning has advanced weather forecasting, but accurate predictions first require identifying the current state of the atmosphere from observational data. In this work, we int…
An implementation of neural simulation-based inference for parameter estimation in ATLAS
ATLAS Collaboration
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation witho…
Simulation-Based Inference Benchmark for Weak Lensing Cosmology
Justine Zeghal, Denise Lanzieri, François Lanusse +5
Standard cosmological analysis, which relies on two-point statistics, fails to extract the full information of the data. This limits our ability to constrain with precision cosmolo…
Learning Diffusion Priors from Observations by Expectation Maximization
François Rozet, Gérôme Andry, François Lanusse +1
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, whi…
Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators
Victor Mangeleer, Gilles Louppe
In climate simulations, small-scale processes shape ocean dynamics but remain computationally expensive to resolve directly. For this reason, their contributions are commonly appro…
Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model
François Rozet, Gilles Louppe
Data assimilation addresses the problem of identifying plausible state trajectories of dynamical systems given noisy or incomplete observations. In geosciences, it presents challen…
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh +4
Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence. Predominantly, the…
Dynamic NeRFs for Soccer Scenes
Sacha Lewin, Maxime Vandegar, Thomas Hoyoux +2
The long-standing problem of novel view synthesis has many applications, notably in sports broadcasting. Photorealistic novel view synthesis of soccer actions, in particular, is of…
Score-based Data Assimilation
François Rozet, Gilles Louppe
Data assimilation, in its most comprehensive form, addresses the Bayesian inverse problem of identifying plausible state trajectories that explain noisy or incomplete observations…
Policy Gradient Algorithms Implicitly Optimize by Continuation
Adrien Bolland, Gilles Louppe, Damien Ernst
Direct policy optimization in reinforcement learning is usually solved with policy-gradient algorithms, which optimize policy parameters via stochastic gradient ascent. This paper…
Balancing Simulation-based Inference for Conservative Posteriors
Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré +2
Conservative inference is a major concern in simulation-based inference. It has been shown that commonly used algorithms can produce overconfident posterior approximations. Balanci…
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping
Norman Marlier, Julien Gustin, Olivier Brüls +1
Robotic grasping in highly noisy environments presents complex challenges, especially with limited prior knowledge about the scene. In particular, identifying good grasping poses w…
Graph-informed simulation-based inference for models of active matter
Namid R. Stillman, Silke Henkes, Roberto Mayor +1
Many collective systems exist in nature far from equilibrium, ranging from cellular sheets up to flocks of birds. These systems reflect a form of active matter, whereby individual…
Simulation-based Bayesian inference for robotic grasping
Norman Marlier, Olivier Brüls, Gilles Louppe
General robotic grippers are challenging to control because of their rich nonsmooth contact dynamics and the many sources of uncertainties due to the environment or sensor noise. I…
Neural posterior estimation for exoplanetary atmospheric retrieval
Malavika Vasist, François Rozet, Olivier Absil +3
Retrieving the physical parameters from spectroscopic observations of exoplanets is key to understanding their atmospheric properties. Exoplanetary atmospheric retrievals are usual…
Adaptive Self-Training for Object Detection
Renaud Vandeghen, Gilles Louppe, Marc Van Droogenbroeck
Deep learning has emerged as an effective solution for solving the task of object detection in images but at the cost of requiring large labeled datasets. To mitigate this cost, se…
A simulator-based autoencoder for focal plane wavefront sensing
Maxime Quesnel, Gilles Orban de Xivry, Olivier Absil +1
Instrumental aberrations strongly limit high-contrast imaging of exoplanets, especially when they produce quasistatic speckles in the science images. With the help of recent advanc…
A deep learning approach for focal-plane wavefront sensing using vortex phase diversity
M. Quesnel, G. Orban de Xivry, G. Louppe +1
The performance of high-contrast imaging instruments is limited by wavefront errors, in particular by non-common path aberrations (NCPAs). Focal-plane wavefront sensing (FPWFS) is…
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
Arnaud Delaunoy, Joeri Hermans, François Rozet +2
Modern approaches for simulation-based inference rely upon deep learning surrogates to enable approximate inference with computer simulators. In practice, the estimated posteriors'…
Robust Hybrid Learning With Expert Augmentation
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu +3
Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data. Similarly to many ML algorithms, hybrid mo…
SAE: Sequential Anchored Ensembles
Arnaud Delaunoy, Gilles Louppe
Computing the Bayesian posterior of a neural network is a challenging task due to the high-dimensionality of the parameter space. Anchored ensembles approximate the posterior by tr…
Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference
François Rozet, Gilles Louppe
In many areas of science, complex phenomena are modeled by stochastic parametric simulators, often featuring high-dimensional parameter spaces and intractable likelihoods. In this…
From global to local MDI variable importances for random forests and when they are Shapley values
Antonio Sutera, Gilles Louppe, Van Anh Huynh-Thu +2
Random forests have been widely used for their ability to provide so-called importance measures, which give insight at a global (per dataset) level on the relevance of input variab…