63.6k citations · 68k across the 67 of their papers we have counts for
4 papers · 2 filters
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…
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…
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…