{
  "id": "1310.6740",
  "version": "v1",
  "published": "2013-10-24T14:15:39.000Z",
  "updated": "2013-10-24T14:15:39.000Z",
  "title": "Active Learning of Linear Embeddings for Gaussian Processes",
  "authors": [
    "Roman Garnett",
    "Michael A. Osborne",
    "Philipp Hennig"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "We propose an active learning method for discovering low-dimensional structure in high-dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and important, but have hitherto presented severe practical difficulties. We further introduce a novel technique for approximately marginalizing GP hyperparameters, yielding marginal predictions robust to hyperparameter mis-specification. Our method offers an efficient means of performing GP regression, quadrature, or Bayesian optimization in high-dimensional spaces.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2013-10-24T14:15:39.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "68T05",
      "I.2.6",
      "I.5.2",
      "G.3"
    ],
    "keywords": [
      "gaussian processes",
      "linear embeddings",
      "active learning",
      "high-dimensional gaussian process",
      "yielding marginal predictions robust"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2013arXiv1310.6740G"
    }
  }
}