The paper shows that for learning algorithms based on preferential compression, the risk can be bounded by the compression complexity term λk/N plus an O(1/√N) term, eliminating the previously assumed logarithmic factor. This bound holds uniformly over all possible values of λk and matches the law‑of‑large‑numbers rate of empirical risk estimation. The authors also demonstrate that the leading λk/N term is necessary and analyze when empirical risk converges at O(1/√N) versus slower or inconsistent rates, while the complexity‑based estimator retains the O(1/√N) rate.
By Simone Garatti, Marco C. Campi
We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distribution over $\mathcal{X} \times \{0,1\}$, as in clas...
arXiv:2610. 26944v2 Announce Type: replace Abstract: Strict replicability requires two independent training samples, processed with the same random seed, to produce exactly the same hypothesis.
By Guangjian Zhang
arXiv:2609.24260v1 Announce Type: cross
Abstract: We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distrib...
By Steve Hanneke, Amirreza Shaeiri
arXiv:2608. 06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.
By Anay Mehrotra
arXiv:2609.36030v1 Announce Type: new
Abstract: The lack of rigorous safety and performance certificates remains a key bottleneck to the deployment of modern learning-based methods. Sample compressio...
By Dario Paccagnan, Marius Tirlea