Pith Number

pith:Z53VJMEV

pith:2026:Z53VJMEVFYT3F7LKH27B3RHNBK

not attested not anchored not stored refs resolved

Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels

Binyamin Perets, Shie Mannor

Optimizing in a reproducing kernel Hilbert space controls false discoveries for structured hypotheses.

arxiv:2605.17559 v1 · 2026-05-17 · stat.ME · cs.AI · q-bio.QM · stat.ML

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\pithnumber{Z53VJMEVFYT3F7LKH27B3RHNBK}

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

By optimizing within a suitable Reproducing Kernel Hilbert Space (RKHS), we introduce a framework that unifies continuous domains, graphs, and hierarchies under a single algorithm through kernel choice alone. Building on this estimator, we provide two decision rules which we prove to control the FDR.

C2weakest assumption

The structure among hypotheses admits a positive-definite kernel representation such that the regularized estimator plus the two decision rules provably control FDR at the target level.

C3one line summary

A kernel-based regularized learning framework for FDR control that unifies arbitrary structures and supplies provably valid decision rules with likelihood-based tuning.

References

44 extracted · 44 resolved · 5 Pith anchors

[1] N. Aronszajn. Theory of reproducing kernels.Transactions of the American Mathematical Society, 68(3):337–404, 1950. ISSN 1088-6850. doi: 10.1090/s0002-9947-1950-0051437-7. URLhttp://dx.doi.org/10.1090 1950 · doi:10.1090/s0002-9947-1950-0051437-7

[2] R. F. Barber and E. J. Candès. Controlling the false discovery rate via knockoffs.The Annals of Statistics, 43(5), Oct. 2015. ISSN 0090-5364. doi: 10.1214/15-aos1337. URL http://dx.doi.org/10.1214/15- 2015 · doi:10.1214/15-aos1337

[3] arXiv preprint arXiv:1801.03896 , year= 2019 · arXiv:1801.03896

[4] Controlling the false discovery rate: A practical and powerful approach to multiple testing 2018 · doi:10.1111/j.2517-6161.1995.tb02031.x

[5] doi: 10.1214/aos/ 1176342360 2001 · doi:10.1214/aos/

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:04:45.845080Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

cf7754b0952e27b2fd6a3ebe1dc4ed0a95b411b34650f3b86d357770efdc1329

Aliases

arxiv: 2605.17559 · arxiv_version: 2605.17559v1 · doi: 10.48550/arxiv.2605.17559 · pith_short_12: Z53VJMEVFYT3 · pith_short_16: Z53VJMEVFYT3F7LK · pith_short_8: Z53VJMEV

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/Z53VJMEVFYT3F7LKH27B3RHNBK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: cf7754b0952e27b2fd6a3ebe1dc4ed0a95b411b34650f3b86d357770efdc1329

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "eb75974cafd05462c929c7eb5ee1a1d1d1b0540325808162129a39f35124d212",
    "cross_cats_sorted": [
      "cs.AI",
      "q-bio.QM",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-17T17:42:56Z",
    "title_canon_sha256": "6d441a0b5fe096f69419526564a38708ac8c4bd07b72558e6a6f8b82d6c135e3"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17559",
    "kind": "arxiv",
    "version": 1
  }
}