Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
We study contextual dynamic pricing in a semiparametric scalar-index valuation model where the latent value is $v_t=μ_\ast(\mathsf c_t)+ξ_t$, with an unknown utility map $μ_\ast$ and an unknown additive noise distribution. The key decision object is the one-dimensional oracle price map $u\mapsto p^\ast(u)$ induced by the scalar index $u=μ_\ast(\mathsf c)$ and the noise tail. Under the $β$-Hölder smoothness of the tail function for $β\geq 2$ and a revenue-geometry condition that gives a unique, stable, interior maximizer, this oracle map is itself $(β-1)$-smooth. We exploit such structure through $\mathsf{ORBIT}$, a modular coarse-to-fine policy that takes a scalar pilot index as input, lo...
| Authors | Yingying Fan, Yuxuan Han, Jinchi Lv et al. |
|---|---|
| Primary category | stat.ML |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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