# Learning in repeated auctions

arXiv: https://arxiv.org/abs/2011.09365 · HTML: https://mlsanity.com/paper/2011.09365/

## Key facts

- Cited by 15 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 15 of 15)

- [Learning to Bid in Contextual First Price Auctions](https://mlsanity.com/paper/2109.03173/) (2021)
- [Online Bidding Algorithms for Return-on-Spend Constrained Advertisers](https://mlsanity.com/paper/2208.13713/) (2022)
- [No-Regret Learning in Bilateral Trade via Global Budget Balance](https://mlsanity.com/paper/2310.12370/) (2023)
- [Fast Rate Learning in Stochastic First Price Bidding](https://mlsanity.com/paper/2107.01835/) (2021)
- [Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning](https://mlsanity.com/paper/2306.07106/) (2023)
- [Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards](https://mlsanity.com/paper/2206.01293/) (2022)
- [Learning and Collusion in Multi-unit Auctions](https://mlsanity.com/paper/2305.17402/) (2023)
- [Intelligent Agents for Auction-based Federated Learning: A Survey](https://mlsanity.com/paper/2404.13244/) (2024)
- [Adaptive maximization of social welfare](https://mlsanity.com/paper/2310.09597/) (2023)
- [Improved learning rates in multi-unit uniform price auctions](https://mlsanity.com/paper/2501.10181/) (2025)
- [Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting](https://mlsanity.com/paper/2501.16018/) (2025)
- [Online Bidding under RoS Constraints without Knowing the Value](https://mlsanity.com/paper/2503.03195/) (2025)
- [Tracking solutions of time-varying variational inequalities](https://mlsanity.com/paper/2406.14059/) (2024)
- [Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization](https://mlsanity.com/paper/2510.19591/) (2025)
- [RTB Formulation Using Point Process](https://mlsanity.com/paper/2308.09122/) (2023)

## Abstract (arXiv)

Online auctions are one of the most fundamental facets of the modern economy and power an industry generating hundreds of billions of dollars a year in revenue. Auction theory has historically focused on the question of designing the best way to sell a single item to potential buyers, with the concurrent objectives of maximizing revenue generated or welfare created. Theoretical results in this area have typically relied on some prior Bayesian knowledge agents were assumed to have on each-other. This assumption is no longer satisfied in new markets such as online advertising: similar items are sold repeatedly, and agents are unaware of each other or might try to manipulate each-other. On the other hand, statistical learning theory now provides tools to supplement those missing pieces of information given enough data, as agents can learn from their environment to improve their strategies. This survey covers recent advances in learning in repeated auctions, starting from the traditional economic study of optimal one-shot auctions with a Bayesian prior. We then focus on the question of learning optimal mechanisms from a dataset of bidders' past values. The sample complexity as well as the computational efficiency of different methods will be studied. We will also investigate online variants where gathering data has a cost to be accounted for, either by seller or buyers ("earning while learning"). Later in the survey, we will further assume that bidders are also adaptive to the mechanism as they interact repeatedly with the same seller. We will show how strategic agents can actually manipulate repeated auctions, to their own advantage. All the questions discussed in this survey are grounded in real-world applications and many of the ideas and algorithms we describe are used every day to power the Internet economy.