# Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions

arXiv: https://arxiv.org/abs/2211.06358 · HTML: https://mlsanity.com/paper/2211.06358/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [More Adaptive Algorithms for Adversarial Bandits](https://mlsanity.com/paper/1801.03265/) (2018)
- [Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions](https://mlsanity.com/paper/2007.04568/) (2020)
- [Contextual Bandits with Cross-learning](https://mlsanity.com/paper/1809.09582/) (2018)
- [Improved Path-length Regret Bounds for Bandits](https://mlsanity.com/paper/1901.10604/) (2019)
- [Taking a hint: How to leverage loss predictors in contextual bandits?](https://mlsanity.com/paper/2003.01922/) (2020)
- [Learning to Bid in Contextual First Price Auctions](https://mlsanity.com/paper/2109.03173/) (2021)

## Built on by (most cited first, 11 of 11)

- [AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games](https://mlsanity.com/paper/2412.10798/) (2024)
- [The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations](https://mlsanity.com/paper/2307.09478/) (2023)
- [Strategizing against No-Regret Learners in First-Price Auctions](https://mlsanity.com/paper/2402.08637/) (2024)
- [Optimal cross-learning for contextual bandits with unknown context distributions](https://mlsanity.com/paper/2401.01857/) (2024)
- [Strategically-Robust Learning Algorithms for Bidding in First-Price Auctions](https://mlsanity.com/paper/2402.07363/) (2024)
- [From No-Regret to Strategically Robust Learning in Repeated Auctions](https://mlsanity.com/paper/2601.03853/) (2026)
- [Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions](https://mlsanity.com/paper/2605.09448/) (2026)
- [Bandits for Sponsored Search Auctions under Unknown Valuation Model: Case Study in E-Commerce Advertising](https://mlsanity.com/paper/2304.00999/) (2023)
- [Optimistic Learning for Communication Networks](https://mlsanity.com/paper/2504.03499/) (2025)
- [Learning Personalized Ad Impact via Contextual Reinforcement Learning under Delayed Rewards](https://mlsanity.com/paper/2510.20055/) (2025)
- [Do Not Trust The Auctioneer: Learning to Bid in Feedback-Manipulated Auctions](https://mlsanity.com/paper/2605.22438/) (2026)

## Abstract (arXiv)

With the advent and increasing consolidation of e-commerce, digital advertising has very recently replaced traditional advertising as the main marketing force in the economy. In the past four years, a particularly important development in the digital advertising industry is the shift from second-price auctions to first-price auctions for online display ads. This shift immediately motivated the intellectually challenging question of how to bid in first-price auctions, because unlike in second-price auctions, bidding one's private value truthfully is no longer optimal. Following a series of recent works in this area, we consider a differentiated setup: we do not make any assumption about other bidders' maximum bid (i.e. it can be adversarial over time), and instead assume that we have access to a hint that serves as a prediction of other bidders' maximum bid, where the prediction is learned through some blackbox machine learning model. We consider two types of hints: one where a single point-prediction is available, and the other where a hint interval (representing a type of confidence region into which others' maximum bid falls) is available. We establish minimax optimal regret bounds for both cases and highlight the quantitatively different behavior between the two settings. We also provide improved regret bounds when the others' maximum bid exhibits the further structure of sparsity. Finally, we complement the theoretical results with demonstrations using real bidding data.