pith:4UBO4OFW
Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
In semiparametric contextual pricing, a scalar-index pilot reduces the problem to learning a one-dimensional smooth oracle price map whose nonparametric cost is minimax sharp.
arxiv:2605.15411 v1 · 2026-05-14 · stat.ML · cs.LG · math.OC
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Claims
The resulting policy achieves regret Õ(T^{(2β-1)/(4β-3)} + √(dT)). For fixed d, we establish a matching lower bound in the horizon dependence, unveiling that the nonparametric oracle-map learning term is minimax sharp.
The revenue-geometry condition that gives a unique, stable, interior maximizer of the expected revenue function for each scalar index u (invoked to guarantee that the oracle price map is well-defined and (β-1)-smooth).
ORBIT learns the (β-1)-smooth oracle price map via local polynomial approximation and bandit convex optimization in a semiparametric contextual pricing model, achieving regret Õ(T^{(2β-1)/(4β-3)} + √(dT)) with a matching lower bound for fixed d.
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| First computed | 2026-05-20T00:00:57.210540Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e502ee38b60b4b6865ea4a1e94d878d4a63f13df9b0ebb116a3d6d44f3911a67
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/4UBO4OFWBNFWQZPKJIPJJWDY2S \
| 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())"
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Canonical record JSON
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