pith:JOIDNAFS
Instance-Adaptive Online Multicalibration
A single efficient algorithm achieves online multicalibration with error rates that automatically adapt to the complexity of the data sequence.
arxiv:2605.09273 v2 · 2026-05-10 · cs.LG
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Claims
We give a single, efficient algorithm which dynamically interpolates between benign and worst-case sequences by adaptively refining a dyadic grid of prediction values. Its error is controlled by the number of leaves in the refinement tree. ... the rate depends on a threshold-complexity measure of the predictable mean process relative to the group family. We show that this dependence is tight up to logarithmic factors.
The analysis assumes that the threshold-complexity measure of the predictable mean process (relative to the given group family) is well-defined and that the algorithm can observe enough information to decide when to refine the dyadic grid without additional side information.
A single online multicalibration algorithm adaptively refines a dyadic grid and achieves instance-dependent rates: O(T^{2/3}) worst-case, O(sqrt T) for marginal stochastic data, and O(sqrt(JT)) for J-piecewise stationary means.
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| First computed | 2026-05-22T01:04:05.798767Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
4b903680b2a9c23f8403ac6c7ff6794559e924842a102d379196fcb4717b86c8
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/JOIDNAFSVHBD7BADVRWH75TZIV \
| 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: 4b903680b2a9c23f8403ac6c7ff6794559e924842a102d379196fcb4717b86c8
Canonical record JSON
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