Source-Aware Dynamic Pricing with Strategic Buyers
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2606. 02595v1 Announce Type: new Abstract: Dynamic pricing in short-term rental (STR) markets presents a distinctive challenge for online learning algorithms: pricing decisions carry significant financial risk, operators require explainability, and market feedback is sparse (one booking outcome per listed night).
arXiv:2605. 09448v2 Announce Type: replace Abstract: We study the operational problem of automated bidding in repeated first-price auctions under budget and return-on-spend (RoS) constraints.
arXiv:2410.14839v5 Announce Type: replace-cross Abstract: We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corpor...
arXiv:2607. 24115v1 Announce Type: cross Abstract: We study the contextual dynamic pricing problem under non-stationarity, where a firm sells products to $T$ sequentially arriving consumers that behave according to an unknown demand model that can change over time.
The paper introduces a doubly robust framework for distributionally robust offline contextual pricing, addressing challenges of distributional shifts and continuous actions. It proposes a localized DR estimator that is computationally efficient and semiparametrically optimal, and a learning algorithm that exploits demand noise smoothness to achieve finite‑sample regret bounds of τ^{-σ/(2σ+1)} for smoothness orders s=1,2. Experiments across varying distribution shifts demonstrate the effectiveness of the proposed methods.
arXiv:2209. 04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data.