{
  "id": 250145,
  "title": "An Optimal Agnostic PAC Algorithm",
  "url": "https://urgent.news/2026/08/06/an-optimal-agnostic-pac-algorithm",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-06T17:57:25.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.06363v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Let $H\\subseteq\\{-1,+1\\}^X$ be a class of finite VC dimension $d\\ge1$. Writing $L$ for the binary risk and $L^*=\\min_{h\\in H}L(h)$, we construct a learner achieving the statistically optimal risk bound: from an i.i.d.\\ sample of size $n$, for every $0",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}