AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The LSA score is proposed, a nonconformity score derived from a posterior predictive tilting identity that shows that the target predictive is an importance-weighted transformation of the source predictive, which is used to derive a direct correction to the Bayesian score.
This work addresses one of the most pervasive obstacles to applying AI in real-world drug development by addressing conformal prediction framework tailored to label shift by weighting conformal scores using marginal label probability ratios and enhancing the trustworthiness of AI-driven predictions.
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