# Learning to Price Against a Moving Target

arXiv: https://arxiv.org/abs/2106.04689 · HTML: https://mlsanity.com/paper/2106.04689/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Strongly Adaptive Online Learning](https://mlsanity.com/paper/1502.07073/) (2015)
- [Dynamic Pricing in High-dimensions](https://mlsanity.com/paper/1609.07574/) (2016)
- [Multidimensional Binary Search for Contextual Decision-Making](https://mlsanity.com/paper/1611.00829/) (2016)
- [Contextual Search via Intrinsic Volumes](https://mlsanity.com/paper/1804.03195/) (2018)
- [Dynamic Pricing with Finitely Many Unknown Valuations](https://mlsanity.com/paper/1807.03288/) (2018)
- [Small-loss bounds for online learning with partial information](https://mlsanity.com/paper/1711.03639/) (2017)
- [Semi-parametric dynamic contextual pricing](https://mlsanity.com/paper/1901.02045/) (2019)
- [Perfect Bayesian Equilibria in Repeated Sales](https://mlsanity.com/paper/1409.3062/) (2014)
- [Why Do Competitive Markets Converge to First-Price Auctions?](https://mlsanity.com/paper/2001.06074/) (2020)

## Built on by (most cited first, 5 of 5)

- [Pricing with Contextual Elasticity and Heteroscedastic Valuation](https://mlsanity.com/paper/2312.15999/) (2023)
- [Market Making without Regret](https://mlsanity.com/paper/2411.13993/) (2024)
- [Non-Stochastic CDF Estimation Using Threshold Queries](https://mlsanity.com/paper/2301.05682/) (2023)
- [Corruption-Robust Lipschitz Contextual Search](https://mlsanity.com/paper/2307.13903/) (2023)
- [From Stream to Pool: Pricing Under the Law of Diminishing Marginal Utility](https://mlsanity.com/paper/2310.19220/) (2023)

## Abstract (arXiv)

In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation. This problem is very well understood when values are stationary (fixed or iid). Here we study the problem where the buyer's value is a moving target, i.e., they change over time either by a stochastic process or adversarially with bounded variation. In either case, we provide matching upper and lower bounds on the optimal revenue loss. Since the target is moving, any information learned soon becomes out-dated, which forces the algorithms to keep switching between exploring and exploiting phases.