Proposes a KL-informed robust optimal transport divergence with stochastic estimation and bootstrap-based SBI for robust inference under joint geometric and TV contamination.
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EC is a Python library that formulates empirical calibration as convex optimization solved in dual form, with added support for multiple objectives, weight clipping, and inexact solutions.
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Robust Simulation Based Inference Through Robust Optimal Transport
Proposes a KL-informed robust optimal transport divergence with stochastic estimation and bootstrap-based SBI for robust inference under joint geometric and TV contamination.
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A Python Library For Empirical Calibration
EC is a Python library that formulates empirical calibration as convex optimization solved in dual form, with added support for multiple objectives, weight clipping, and inexact solutions.