{
  "id": 16975,
  "url": "https://arxiv.org/abs/2608.04382v1",
  "title": "Non-asymptotic implicit bias of logistic regression at early-stage gradient descent dynamics",
  "summary": "Gradient descent has been of particular interest in modern machine learning beyond sole focus on optimization. Implicit bias emerging from optimization, though not being encoded by the learning objective, often prevents from overfitting to spurious patterns. A typical instance is the max-margin implicit bias of a linear classifier, widely established for exponentially tailed loss functions. Even after having a given dataset separated, the parameter vector continues to evolve towards the max-marg",
  "authors": "Han Bao",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T02:39:49.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16975",
  "original_url": "https://arxiv.org/abs/2608.04382v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}