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.
arXiv preprint arXiv:2405.06866 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ML 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
DC-TNN decomposes tensors into low-rank core plus sparse refinement fed to coupled neural channels, yielding non-asymptotic risk bounds and the first distribution-free conformal procedure for selecting among tensor decompositions.
citing papers explorer
-
Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
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.
-
Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection
DC-TNN decomposes tensors into low-rank core plus sparse refinement fed to coupled neural channels, yielding non-asymptotic risk bounds and the first distribution-free conformal procedure for selecting among tensor decompositions.