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pith:CDI5UTGM

pith:2026:CDI5UTGMP76PZ5VCXWSLZCOS45
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Online Conformal Prediction: Enforcing monotonicity via Online Optimization

Ambuj Tewari, Eduardo Ochoa Rivera

Online conformal prediction methods produce nested sets across coverage levels by using low-regret online optimization to control quantile errors.

arxiv:2605.12668 v1 · 2026-05-12 · stat.ML · cs.LG

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4 Citations open
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Claims

C1strongest claim

Our approaches leverage an online optimization perspective with small regret that translates to quantile estimation error control while enforcing nestedness of prediction sets.

C2weakest assumption

That the online optimization framework with small regret directly enforces both the coverage guarantees and the strict nestedness of prediction sets across levels without post-hoc adjustments or loss of efficiency.

C3one line summary

Two novel online conformal prediction algorithms enforce nested prediction sets across coverage levels using online optimization with regret bounds for quantile error control.

References

29 extracted · 29 resolved · 1 Pith anchors

[1] The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
[2] European conference on machine learning , pages= 2002
[3] Algorithmic learning in a random world , author=. 2005 , publisher= 2005
[4] A Tutorial on Conformal Prediction. , author=. Journal of Machine Learning Research , volume=
[5] The Annals of Statistics , volume= 2023
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First computed 2026-05-18T03:09:50.290047Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

10d1da4ccc7ffcfcf6a2bda4bc89d2e76466e56bd53747cacc6e96cdcc69a8b9

Aliases

arxiv: 2605.12668 · arxiv_version: 2605.12668v1 · doi: 10.48550/arxiv.2605.12668 · pith_short_12: CDI5UTGMP76P · pith_short_16: CDI5UTGMP76PZ5VC · pith_short_8: CDI5UTGM
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/CDI5UTGMP76PZ5VCXWSLZCOS45 \
  | 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: 10d1da4ccc7ffcfcf6a2bda4bc89d2e76466e56bd53747cacc6e96cdcc69a8b9
Canonical record JSON
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  "metadata": {
    "abstract_canon_sha256": "104d0b6723736efeb5d3c6e9b562f2a89326931c4abc1106790ec16b41e3e42e",
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-05-12T19:18:43Z",
    "title_canon_sha256": "83cf5893574120c7c672b5ece5c463edfdd1d2a0a2f4b739b1cfa1db0cecd25d"
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  "source": {
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    "kind": "arxiv",
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