AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This scheme removes the dual factor entirely, including in the realizable case, from all previously known bounded-size constructions, agnostic and even realizable.
The threshold part of Question 4 of the COLT 2025 open problem "Data Selection for Regression Tasks" is resolved, and an erroneous claim circulating in a recent unrefereed preprint is correct.
How much data must a fixed learner retain? Hanneke, Moran, Shlimovich and Yehudayoff (COLT 2025) posed this question for linear regression with the minimum-norm empirical risk minimizer. A selector sees a finite dataset $D\subseteq R^d\times R$, keeps at most $n$ examples with nonnegative weights, and $F_w(d,n)$ is the...
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