10 citations · 11 across the 4 of their papers we have counts for
4 papers
Low-Budget Simulation-Based Inference with Bayesian Neural Networks
Arnaud Delaunoy, Maxence de la Brassinne Bonardeaux, Siddharth Mishra-Sharma +1
Simulation-based inference methods have been shown to be inaccurate in the data-poor regime, when training simulations are limited or expensive. Under these circumstances, the infe…
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…
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…
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'…