AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is argued that true fairness requires addressing bias at the intersections of protected groups, and a unified framework that leverages Mixed-Integer Optimization (MIO) to train intersectionally fair and intrinsically interpretable classifiers is proposed.
A special semimartingale admits a unique decomposition $X=X_0+M+A$ into a local martingale $M$ and a predictable finite-variation part $A$. We consider the identification of $A$ when $X$ is observed only through repeated cross-sections. The estimand is then the projection of the sampled predictable compensator onto the...
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