# Learning to Bid in Repeated First-Price Auctions with Budgets

arXiv: https://arxiv.org/abs/2304.13477 · HTML: https://mlsanity.com/paper/2304.13477/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2023.

## Built on by (most cited first, 14 of 14)

- [No-Regret Algorithms in non-Truthful Auctions with Budget and ROI Constraints](https://mlsanity.com/paper/2404.09832/) (2024)
- [Strategically-Robust Learning Algorithms for Bidding in First-Price Auctions](https://mlsanity.com/paper/2402.07363/) (2024)
- [No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization](https://mlsanity.com/paper/2405.06575/) (2024)
- [Joint Value Estimation and Bidding in Repeated First-Price Auctions](https://mlsanity.com/paper/2502.17292/) (2025)
- [Learning to Bid in Non-Stationary Repeated First-Price Auctions](https://mlsanity.com/paper/2501.13358/) (2025)
- [Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets](https://mlsanity.com/paper/2306.07709/) (2023)
- [Online Learning in Contextual Second-Price Pay-Per-Click Auctions](https://mlsanity.com/paper/2310.05047/) (2023)
- [Nearly Tight Bounds for Cross-Learning Contextual Bandits with Graphical Feedback](https://mlsanity.com/paper/2502.04678/) (2025)
- [Episodic Contextual Bandits with Knapsacks under Conversion Models](https://mlsanity.com/paper/2507.06859/) (2025)
- [Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions](https://mlsanity.com/paper/2605.09448/) (2026)
- [Plan-Driven Adaptive Bidding for First-Price Auctions with Budget Constraints under Nonstationarity](https://mlsanity.com/paper/2505.02796/) (2025)
- [Guiding the Recommender: Information-Aware Auto-Bidding for Content Promotion](https://mlsanity.com/paper/2601.20422/) (2026)
- [Is Online Linear Optimization Sufficient for Strategic Robustness?](https://mlsanity.com/paper/2602.12253/) (2026)
- [Strategic Bidding in 6G Spectrum Auctions with Large Language Models](https://mlsanity.com/paper/2604.24156/) (2026)

## Abstract (arXiv)

Budget management strategies in repeated auctions have received growing attention in online advertising markets. However, previous work on budget management in online bidding mainly focused on second-price auctions. The rapid shift from second-price auctions to first-price auctions for online ads in recent years has motivated the challenging question of how to bid in repeated first-price auctions while controlling budgets. In this work, we study the problem of learning in repeated first-price auctions with budgets. We design a dual-based algorithm that can achieve a near-optimal widetildeO(√T) regret with full information feedback where the maximum competing bid is always revealed after each auction. We further consider the setting with one-sided information feedback where only the winning bid is revealed after each auction. We show that our modified algorithm can still achieve an widetildeO(√T) regret with mild assumptions on the bidder's value distribution. Finally, we complement the theoretical results with numerical experiments to confirm the effectiveness of our budget management policy.