{
  "id": "2305.00520",
  "version": "v1",
  "published": "2023-04-30T16:36:57.000Z",
  "updated": "2023-04-30T16:36:57.000Z",
  "title": "The ART of Transfer Learning: An Adaptive and Robust Pipeline",
  "authors": [
    "Boxiang Wang",
    "Yunan Wu",
    "Chenglong Ye"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms. We establish the non-asymptotic learning theory of ART, providing a provable theoretical guarantee for achieving adaptive transfer while preventing negative transfer. Additionally, we introduce an ART-integrated-aggregating machine that produces a single final model when multiple candidate algorithms are considered. We demonstrate the promising performance of ART through extensive empirical studies on regression, classification, and sparse learning. We further present a real-data analysis for a mortality study.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-04-30T16:36:57.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "transfer learning",
      "robust pipeline",
      "multiple candidate algorithms",
      "single final model",
      "auxiliary data resources"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}