NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Introduces pessimistic and opportunistic policies for offline dynamic pricing under no price coverage via partial identification from demand monotonicity, with finite-sample regret bounds that recover standard rates when coverage exists.
citing papers explorer
-
Nonparametric Bayesian Policy Learning
NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.
-
A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing
Introduces pessimistic and opportunistic policies for offline dynamic pricing under no price coverage via partial identification from demand monotonicity, with finite-sample regret bounds that recover standard rates when coverage exists.