{
  "abstract": "Supersaturated screening experiments study many candidate factors with few runs. The experimenter must first identify the active factors, then optimize the response. One-shot regularized regression tends to select many factors, and the second-order response surface model in the selected factors is then large and needs many further runs to fit. We propose Sequential Supersaturated Screening ($S^3$), a two-stage framework for supersaturated designs. $S^3$ builds each screening round by coordinate exchange under a new positive-cone design criterion that uses no practitioner-chosen Welch calibration constant. It then removes low-importance factors one round at a time, using a graduated quantile rule. The criterion scores a design by how far its column correlations sit above the Welch lower bound, and it adapts to the current number of runs and candidate factors. We give a round-by-round bound on the probability that a noise factor is ever fixed, under conditions on noise survival and commitment at each round, together with an explicit upper bound on the total number of Stage 1 runs. Across simulated screening problems and the Borehole benchmark, $S^3$ achieves lower Type I error and higher $F_1$ scores than one-shot cross-validated Lasso (LassoCV). It also improves optimization quality in most settings and runs faster.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.37756",
  "author_total": 4,
  "authors": [
    {
      "name": "Songqiao Han"
    },
    {
      "name": "Kalliopi Mylona"
    },
    {
      "name": "Steven Gilmour"
    },
    {
      "name": "Matteo Borrotti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/adar5rf6qkfyh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.37756",
  "fulltext_url": null,
  "id": "adar5rf6qkfyh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.37756",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1803|Optimal Experimental Design Methods"
    ]
  ],
  "openalex": "W7214922880",
  "pdf_url": "https://arxiv.org/pdf/2609.37756",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/adar5rf6qkfyh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:41Z",
      "native_id": "2609.37756",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.37756"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Sequential Supersaturated Screening Experiments",
  "title_text": "Sequential Supersaturated Screening Experiments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:41Z",
  "url": "https://research.draconitic.com/paper/adar5rf6qkfyh",
  "venue": "arXiv",
  "versions": []
}