# Perishability of Data: Dynamic Pricing under Varying-Coefficient Models

arXiv: https://arxiv.org/abs/1701.03537 · HTML: https://mlsanity.com/paper/1701.03537/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2017.

## Built on by (most cited first, 19 of 19)

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- [Contextual Online Bilateral Trade](https://mlsanity.com/paper/2602.12903/) (2026)
- [Learning to Price Homogeneous Data](https://mlsanity.com/paper/2407.05484/) (2024)
- [Multi-Task Dynamic Pricing in Credit Market with Contextual Information](https://mlsanity.com/paper/2410.14839/) (2024)
- [Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand](https://mlsanity.com/paper/2605.05609/) (2026)
- [Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning](https://mlsanity.com/paper/2310.07558/) (2023)
- [Revenue Maximization Under Sequential Price Competition Via The Estimation Of s-Concave Demand Functions](https://mlsanity.com/paper/2503.16737/) (2025)
- [DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization](https://mlsanity.com/paper/2512.03607/) (2025)
- [Learning to Sell a Focal-ancillary Combination](https://mlsanity.com/paper/2207.11545/) (2022)
- [Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model](https://mlsanity.com/paper/2303.15652/) (2023)
- [Bandit Profit-maximization for Targeted Marketing](https://mlsanity.com/paper/2403.01361/) (2024)
- [Contextual Dynamic Pricing with Heterogeneous Buyers](https://mlsanity.com/paper/2512.09513/) (2025)
- [PREFER: Personalized Review Summarization with Online Preference Learning](https://mlsanity.com/paper/2605.05911/) (2026)

## Abstract (arXiv)

We consider a firm that sells a large number of products to its customers in an online fashion. Each product is described by a high dimensional feature vector, and the market value of a product is assumed to be linear in the values of its features. Parameters of the valuation model are unknown and can change over time. The firm sequentially observes a product's features and can use the historical sales data (binary sale/no sale feedbacks) to set the price of current product, with the objective of maximizing the collected revenue. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. We propose a pricing policy based on projected stochastic gradient descent (PSGD) and characterize its regret in terms of time T, features dimension d, and the temporal variability in the model parameters, δt. We consider two settings. In the first one, feature vectors are chosen antagonistically by nature and we prove that the regret of PSGD pricing policy is of order O(√T + sumt=1T √tδt). In the second setting (referred to as stochastic features model), the feature vectors are drawn independently from an unknown distribution. We show that in this case, the regret of PSGD pricing policy is of order O(d2 log T + sumt=1T tδt/d).