pith:VS44BEYE
In-Context Learning for Data-Driven Censored Inventory Control
ICGPS combines offline meta-trained generative models with online in-context autoregressive generation to bound Bayesian regret in decision-dependent censored inventory control and outperforms baselines under mismatch.
arxiv:2605.14840 v1 · 2026-05-14 · cs.LG · math.OC · stat.ML
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\pithnumber{VS44BEYEV4PPBYXOI7ZJILFYNN}
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Record completeness
Claims
The Bayesian regret of ICGPS with a learned completion kernel is bounded by the Bayesian regret of a TS benchmark with the ideal completion kernel plus a deployment penalty scaling as √T times the square root of the completion mismatch; for R-NV this yields sublinear Bayesian regret by reduction to bandit convex optimization feedback.
Under reasonable coverage and stability assumptions, the online completion mismatch is controlled by the offline censored predictive mismatch so that offline predictive quality transfers to online performance.
ICGPS combines offline meta-trained generative models with online in-context autoregressive generation to bound Bayesian regret in decision-dependent censored inventory control and outperforms baselines under mismatch.
Receipt and verification
| First computed | 2026-05-17T23:38:56.422398Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
acb9c09304af1ef0e2ee47f2942cb86b40ab408926fbe3a537209bddc4737e99
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/VS44BEYEV4PPBYXOI7ZJILFYNN \
| 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: acb9c09304af1ef0e2ee47f2942cb86b40ab408926fbe3a537209bddc4737e99
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
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