Pith Number

pith:F5JPWDPM

pith:2026:F5JPWDPMA7VA75DVQFQFXQ63J2

not attested not anchored not stored refs pending

Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks

Azza Fadhel, Jana Doppa, The Hung Tran, Trong Nghia Hoang

Meta-learning optimization bias from Gaussian process synthetic tasks improves surrogate ranking for offline black-box optimization in small data regimes.

arxiv:2604.12325 v2 · 2026-04-14 · cs.LG · cs.AI

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\usepackage{pith}
\pithnumber{F5JPWDPMA7VA75DVQFQFXQ63J2}

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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

✓ Portable graph bundle live · download bundle · merged state

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

Across diverse continuous and discrete offline optimization benchmarks, OptBias consistently outperforms state-of-the-art baselines in small data regimes.

C2weakest assumption

That optimization bias learned from Gaussian process synthetic tasks will transfer to and improve ranking on real-world small datasets whose underlying functions may differ substantially from the GP prior.

C3one line summary

OptBias meta-learns reusable optimization bias from Gaussian process synthetic tasks to improve surrogate ranking performance on small offline black-box optimization datasets.

Receipt and verification

First computed	2026-05-20T00:03:11.219849Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2f52fb0dec07ea0ff47581605bc3db4eac0faeb75a164817758560f6ea13f3e1

Aliases

arxiv: 2604.12325 · arxiv_version: 2604.12325v2 · doi: 10.48550/arxiv.2604.12325 · pith_short_12: F5JPWDPMA7VA · pith_short_16: F5JPWDPMA7VA75DV · pith_short_8: F5JPWDPM

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/F5JPWDPMA7VA75DVQFQFXQ63J2 \
  | 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: 2f52fb0dec07ea0ff47581605bc3db4eac0faeb75a164817758560f6ea13f3e1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "039e70f6c3b5745a7a9436b905dcdd38064f24b0ee9a4de85dd59d07fe403d8a",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-14T06:00:30Z",
    "title_canon_sha256": "38c8b8a5c1fe9582a6199dc276bb5ce01072553e3a73de0185bb14269c9f179e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.12325",
    "kind": "arxiv",
    "version": 2
  }
}