# Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions

arXiv: https://arxiv.org/abs/2002.11137 · HTML: https://mlsanity.com/paper/2002.11137/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 30 of 34)

- [Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization](https://mlsanity.com/paper/2102.11050/) (2021)
- [Learning in Stackelberg Games with Non-myopic Agents](https://mlsanity.com/paper/2208.09407/) (2022)
- [A Scalable Neural Network for DSIC Affine Maximizer Auction Design](https://mlsanity.com/paper/2305.12162/) (2023)
- [Policy Optimization Using Semi-parametric Models for Dynamic Pricing](https://mlsanity.com/paper/2109.06368/) (2021)
- [Distribution-free Contextual Dynamic Pricing](https://mlsanity.com/paper/2109.07340/) (2021)
- [A Context-Integrated Transformer-Based Neural Network for Auction Design](https://mlsanity.com/paper/2201.12489/) (2022)
- [Multi-channel Autobidding with Budget and ROI Constraints](https://mlsanity.com/paper/2302.01523/) (2023)
- [No-regret Learning in Repeated First-Price Auctions with Budget Constraints](https://mlsanity.com/paper/2205.14572/) (2022)
- [Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions](https://mlsanity.com/paper/2110.03906/) (2021)
- [Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model](https://mlsanity.com/paper/2303.08613/) (2023)
- [Learning and Collusion in Multi-unit Auctions](https://mlsanity.com/paper/2305.17402/) (2023)
- [A Parametric Contextual Online Learning Theory of Brokerage](https://mlsanity.com/paper/2407.01566/) (2024)
- [Dynamic Pricing in the Linear Valuation Model using Shape Constraints](https://mlsanity.com/paper/2502.05776/) (2025)
- [Learning to Manipulate a Commitment Optimizer](https://mlsanity.com/paper/2302.11829/) (2023)
- [Multi-Platform Budget Management in Ad Markets with Non-IC Auctions](https://mlsanity.com/paper/2306.07352/) (2023)
- [Contextual Dynamic Pricing with Strategic Buyers](https://mlsanity.com/paper/2307.04055/) (2023)
- [Learning in Repeated Multi-Unit Pay-As-Bid Auctions](https://mlsanity.com/paper/2307.15193/) (2023)
- [Optimal Private Payoff Manipulation against Commitment in Extensive-form Games](https://mlsanity.com/paper/2206.13119/) (2022)
- [Stochastic Bandits with ReLU Neural Networks](https://mlsanity.com/paper/2405.07331/) (2024)
- [Minimax Optimality in Contextual Dynamic Pricing with General Valuation Models](https://mlsanity.com/paper/2406.17184/) (2024)
- [Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting](https://mlsanity.com/paper/2501.16018/) (2025)
- [Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand](https://mlsanity.com/paper/2605.05609/) (2026)
- [Dynamic Pricing and Demand Learning on a Large Network of Products: A PAC-Bayesian Approach](https://mlsanity.com/paper/2111.00790/) (2021)
- [Online Allocation and Learning in the Presence of Strategic Agents](https://mlsanity.com/paper/2209.12112/) (2022)
- [A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design](https://mlsanity.com/paper/2210.10278/) (2022)
- [Online Stackelberg Optimization via Nonlinear Control](https://mlsanity.com/paper/2406.18805/) (2024)
- [Revenue Maximization Under Sequential Price Competition Via The Estimation Of s-Concave Demand Functions](https://mlsanity.com/paper/2503.16737/) (2025)
- [Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model](https://mlsanity.com/paper/2303.15652/) (2023)
- [Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning](https://mlsanity.com/paper/2407.04898/) (2024)
- [Parameter-Adaptive Dynamic Pricing](https://mlsanity.com/paper/2503.00929/) (2025)

## Abstract (arXiv)

Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions. Buyers' valuations for an item depend on the context that describes the item. However, the seller is not aware of the relationship between the context and buyers' valuations, i.e., buyers' preferences. The seller's goal is to design a learning policy to set reserve prices via observing the past sales data, and her objective is to minimize her regret for revenue, where the regret is computed against a clairvoyant policy that knows buyers' heterogeneous preferences. Given the seller's goal, utility-maximizing buyers have the incentive to bid untruthfully in order to manipulate the seller's learning policy. We propose learning policies that are robust to such strategic behavior. These policies use the outcomes of the auctions, rather than the submitted bids, to estimate the preferences while controlling the long-term effect of the outcome of each auction on the future reserve prices. When the market noise distribution is known to the seller, we propose a policy called Contextual Robust Pricing (CORP) that achieves a T-period regret of O(dlog(Td) log (T)), where d is the dimension of the contextual information. When the market noise distribution is unknown to the seller, we propose two policies whose regrets are sublinear in T.