A Wasserstein-GAN-inspired method infers physically interpretable transformation parameters to correct sensor-induced distribution drift in an unsupervised manner.
Likelihood-free inference with an improved cross-entropy estimator
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abstract
We extend recent work (Brehmer, et. al., 2018) that use neural networks as surrogate models for likelihood-free inference. As in the previous work, we exploit the fact that the joint likelihood ratio and joint score, conditioned on both observed and latent variables, can often be extracted from an implicit generative model or simulator to augment the training data for these surrogate models. We show how this augmented training data can be used to provide a new cross-entropy estimator, which provides improved sample efficiency compared to previous loss functions exploiting this augmented training data.
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2026 2verdicts
CONDITIONAL 2representative citing papers
Simulations indicate that 2500 LSST strong lenses can exclude 74% and 36% of the prior volume on halo mass function parameters at 3σ and 5σ, with sensitivity from both high- and low-mass halos plus line-of-sight contributions.
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LSST Strong Lensing Systems Dark Matter Sensitivity Analysis with Neural Ratio Estimators
Simulations indicate that 2500 LSST strong lenses can exclude 74% and 36% of the prior volume on halo mass function parameters at 3σ and 5σ, with sensitivity from both high- and low-mass halos plus line-of-sight contributions.