Boosting converts weak hypotheses with a small edge over random guessing into highly accurate predictors, but the expressive power of the resulting classifier can depend strongly on the structure of t...
arXiv:2610. 31890v1 Announce Type: new Abstract: We study the PAC-learnability of function classes that map inputs to probability distributions over a finite space.
By Mihir Dhanakshirur, Ambuj Tewari
arXiv:2607. 05791v1 Announce Type: cross Abstract: Boosting is a fundamental technique for generically improving the accuracy of learning algorithms (Schapire 1989).
By Addison Prairie, Li-Yang Tan
The paper introduces a statistical learning theory for size generalization in models that handle variable-size structured data such as sets and graphs. It proves an exponential separation in sample complexity: polynomial in-domain learning yields only logarithmic decay in size-generalization error, while an exponential source condition restores polynomial decay. These results provide dimension‑free scaling laws and optimal minimax bounds for transferring learning across input sizes.
By Behrooz Tahmasebi, Melanie Weber
arXiv:2506. 16704v3 Announce Type: replace Abstract: We study a fundamental question of domain generalization: given a family of domains (i.
By Cynthia Dwork, Lunjia Hu, Han Shao
arXiv:2606. 11149v1 Announce Type: new Abstract: We study the problem of learning a drifting concept in the presence of Massart noise.
By Mingchen Ma, Guyang Cao, Jelena Diakonikolas, Ilias Diakonikolas