pith. sign in
Pith Number

pith:EBRDFLDQ

pith:2026:EBRDFLDQDJ765YNXS4L63JMM63
not attested not anchored not stored refs resolved

Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference

Eunho Jeong, Hyeonjin Kim, Joon Jang, Kyu Sung Choi

SPIN improves posterior inference in misspecified simulation-based inference by using information-preserving domain transfer with unlabeled real-world data.

arxiv:2605.05652 v2 · 2026-05-07 · cs.LG

Add to your LaTeX paper
\usepackage{pith}
\pithnumber{EBRDFLDQDJ765YNXS4L63JMM63}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp
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

SPIN improves real-world posterior inference in misspecified SBI by translating labeled simulator observations toward the real-world domain and back while using original labels to preserve parameter-relevant mutual information, with gains becoming clearer as misspecification increases.

C2weakest assumption

The assumption that the learned real-to-simulator transport map preserves the mutual information between observations and parameters sufficiently well that the downstream SBI posterior remains accurate, even though no real-world parameter labels are available during training or testing.

C3one line summary

SPIN performs bidirectional domain transfer in SBI to retain parameter mutual information from unlabeled real observations, improving real-world posterior inference under increasing misspecification.

References

56 extracted · 56 resolved · 0 Pith anchors

[1] The frontier of simulation-based inference 2020 · doi:10.1073/pnas.1912789117
[2] Simulation-based inference: A practical guide.arXiv preprint arXiv:2508.12939, 2025
[3] Fastϵ-free inference of simulation models with Bayesian conditional density estimation 2016
[4] Greenberg, Marcel Nonnenmacher, and Jakob H
[5] URLhttps://proceedings.mlr.press/v97/greenberg19a.html

Formal links

2 machine-checked theorem links

Receipt and verification
First computed 2026-05-20T00:01:42.900026Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

206232ac701a7feee1b79717eda58cf6e317b16e889daabed59ba8a9f1dbfe18

Aliases

arxiv: 2605.05652 · arxiv_version: 2605.05652v2 · doi: 10.48550/arxiv.2605.05652 · pith_short_12: EBRDFLDQDJ76 · pith_short_16: EBRDFLDQDJ765YNX · pith_short_8: EBRDFLDQ
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/EBRDFLDQDJ765YNXS4L63JMM63 \
  | 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: 206232ac701a7feee1b79717eda58cf6e317b16e889daabed59ba8a9f1dbfe18
Canonical record JSON
{
  "metadata": {
    "abstract_canon_sha256": "181750da74775fecc00cdbbedda3b5d91b9983ccd91e3ebcbf66f87c4d8db1f7",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-07T04:06:53Z",
    "title_canon_sha256": "4b1a71cd114af8428c4c194048d9f6136742e6c7c7f76a5ed9acef68b3b5f1cf"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.05652",
    "kind": "arxiv",
    "version": 2
  }
}