pith:CDI5UTGM
Online Conformal Prediction: Enforcing monotonicity via Online Optimization
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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Record completeness
Claims
Our approaches leverage an online optimization perspective with small regret that translates to quantile estimation error control while enforcing nestedness of prediction sets.
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.
Two novel online conformal prediction algorithms enforce nested prediction sets across coverage levels using online optimization with regret bounds for quantile error control.
References
Receipt and verification
| 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
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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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