63.6k citations · 68k across the 67 of their papers we have counts for
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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…
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
Joeri Hermans, Arnaud Delaunoy, François Rozet +3
We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations. Our res…
Simulation-based Bayesian inference for multi-fingered robotic grasping
Norman Marlier, Olivier Brüls, Gilles Louppe
Multi-fingered robotic grasping is an undeniable stepping stone to universal picking and dexterous manipulation. Yet, multi-fingered grippers remain challenging to control because…
Truncated Marginal Neural Ratio Estimation
Benjamin Kurt Miller, Alex Cole, Patrick Forré +2
Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood. Performing Bayesian parameter infere…
Diffusion Priors In Variational Autoencoders
Antoine Wehenkel, Gilles Louppe
Among likelihood-based approaches for deep generative modelling, variational autoencoders (VAEs) offer scalable amortized posterior inference and fast sampling. However, VAEs are a…
Focal Plane Wavefront Sensing using Machine Learning: Performance of Convolutional Neural Networks compared to Fundamental Limits
G. Orban de Xivry, M. Quesnel, P. -O. Vanberg +2
Focal plane wavefront sensing (FPWFS) is appealing for several reasons. Notably, it offers high sensitivity and does not suffer from non-common path aberrations (NCPA). The price t…
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
Thibaut Théate, Antoine Wehenkel, Adrien Bolland +2
The distributional reinforcement learning (RL) approach advocates for representing the complete probability distribution of the random return instead of only modelling its expectat…
HNPE: Leveraging Global Parameters for Neural Posterior Estimation
Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe +1
Inferring the parameters of a stochastic model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is stron…