Contextual dynamic pricing aims to set personalized prices based on sequential interactions
with customers. At each time period, a customer who is interested in purchasing a product …

We consider dynamic multi-product pricing and assortment problems under an unknown
demand over T periods, where in each period, the seller decides on the price for each …

In feature-based dynamic pricing, a seller sets appropriate prices for a sequence of products
(described by feature vectors) on the fly by learning from the binary outcomes of previous …

In today's data-rich environment, context-based dynamic pricing has gained much attention.
To model the demand as a function of price and context, the existing literature either adopts …

Dynamic pricing is a fast-moving research area in machine learning and operations
management. A lot of work has been done for this problem with known noise. In this paper …

We study the problem of online dynamic pricing with two types of fairness constraints: a
“procedural fairness” which requires the “proposed” prices to be equal in expectation among …

Motivated by personalized healthcare and other applications involving sensitive data, we
study online exploration in reinforcement learning with differential privacy (DP) constraints …

Personalized pricing, which involves tailoring prices based on individual characteristics, is
commonly used by firms to implement a consumer-specific pricing policy. In this process …

In this work, we investigate the problem of how to bid in repeated contextual first price
auctions for a single learner (the bidder). Concretely, at each time t, the learner receives a …

We consider dynamic pricing with covariates under a generalized linear demand model: a
seller can dynamically adjust the price of a product over a horizon of $ T $ time periods, and …