SCARCE uses learned latent representations and adaptive thresholding to achieve 400-500x lower error than traditional subset simulation for MNIST misclassification and low relative error on LLM jailbreak probabilities.
Game-theoretic statistics and safe anytime- valid inference.arXiv preprint arXiv:2006.04292, 2020
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SCARCE: Scalable Cascade Analysis for Rare-event Characterisation via Embeddings
SCARCE uses learned latent representations and adaptive thresholding to achieve 400-500x lower error than traditional subset simulation for MNIST misclassification and low relative error on LLM jailbreak probabilities.