arXiv:2610. 09206v2 Announce Type: replace Abstract: Loss-difference conditional mutual information (ld-CMI) uses the smallest of the standard observations in the supersample hierarchy of generalization bounds: it measures what a learner's loss differences reveal about which candidate of each pair it was trained on.
By Hazar Yueksel
arXiv:2608. 15472v1 Announce Type: cross Abstract: The problem of networked information aggregation, studied in Kearns et al.
By Ambar Pal
arXiv:2610.29289v1 Announce Type: new
Abstract: Why large language models trained via log-loss minimization over massive and diverse datasets exhibit broad capabilities and generalizable skills is no...
By Qian Li, Shang-Hua Teng
arXiv:2610. 29425v1 Announce Type: new Abstract: Understanding generalization of randomized learning algorithms remains a central challenge.
By Mohammad Hossein Momeni Hamaneh, Mohammad Mahdi Mojahedian, Mohammad Hossein Yassaee
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
arXiv:2510. 07758v3 Announce Type: replace Abstract: Sharpness (of the loss minima) is widely believed to be a good indicator of generalization of neural networks.
By Qiaozhe Zhang, Jun Sun, Ruijie Zhang, Yingzhuang Liu