# The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations

arXiv: https://arxiv.org/abs/2307.09478 · HTML: https://mlsanity.com/paper/2307.09478/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 24 of 24)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Bandits and Experts in Metric Spaces](https://mlsanity.com/paper/1312.1277/) (2013)
- [Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback](https://mlsanity.com/paper/1409.8428/) (2014)
- [Smoothed Analysis with Adaptive Adversaries](https://mlsanity.com/paper/2102.08446/) (2021)
- [Smoothed Online Learning is as Easy as Statistical Learning](https://mlsanity.com/paper/2202.04690/) (2022)
- [Smoothed Analysis of Online and Differentially Private Learning](https://mlsanity.com/paper/2006.10129/) (2020)
- [Online learning in repeated auctions](https://mlsanity.com/paper/1511.05720/) (2015)
- [Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization](https://mlsanity.com/paper/1711.03091/) (2017)
- [Learning to Bid Without Knowing your Value](https://mlsanity.com/paper/1711.01333/) (2017)
- [Optimal No-regret Learning in Repeated First-price Auctions](https://mlsanity.com/paper/2003.09795/) (2020)
- [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)
- [Bilateral Trade: A Regret Minimization Perspective](https://mlsanity.com/paper/2109.12974/) (2021)
- [Auctions Between Regret-Minimizing Agents](https://mlsanity.com/paper/2110.11855/) (2021)
- [A Regret Analysis of Bilateral Trade](https://mlsanity.com/paper/2102.08754/) (2021)
- [Learning to Bid in Contextual First Price Auctions](https://mlsanity.com/paper/2109.03173/) (2021)
- [Repeated Bilateral Trade Against a Smoothed Adversary](https://mlsanity.com/paper/2302.10805/) (2023)
- [Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions](https://mlsanity.com/paper/2009.06136/) (2020)
- [Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret](https://mlsanity.com/paper/2202.10997/) (2022)
- [Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions](https://mlsanity.com/paper/2211.06358/) (2022)
- [Correlated and Coarse equilibria of Single-item auctions](https://mlsanity.com/paper/1601.07702/) (2016)
- [A Nearest Neighbor Characterization of Lebesgue Points in Metric Measure Spaces](https://mlsanity.com/paper/2007.03937/) (2020)
- [Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions](https://mlsanity.com/paper/2110.03906/) (2021)
- [Fast Rate Learning in Stochastic First Price Bidding](https://mlsanity.com/paper/2107.01835/) (2021)

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

- [No-Regret Learning in Bilateral Trade via Global Budget Balance](https://mlsanity.com/paper/2310.12370/) (2023)
- [Strategically-Robust Learning Algorithms for Bidding in First-Price Auctions](https://mlsanity.com/paper/2402.07363/) (2024)
- [Nearly Tight Regret Bounds for Profit Maximization in Bilateral Trade](https://mlsanity.com/paper/2509.22563/) (2025)
- [Joint Value Estimation and Bidding in Repeated First-Price Auctions](https://mlsanity.com/paper/2502.17292/) (2025)
- [Selling Joint Ads: A Regret Minimization Perspective](https://mlsanity.com/paper/2409.07819/) (2024)
- [Market Making without Regret](https://mlsanity.com/paper/2411.13993/) (2024)
- [Learning to Bid in Non-Stationary Repeated First-Price Auctions](https://mlsanity.com/paper/2501.13358/) (2025)
- [A Parametric Contextual Online Learning Theory of Brokerage](https://mlsanity.com/paper/2407.01566/) (2024)
- [Learning Thresholds with Latent Values and Censored Feedback](https://mlsanity.com/paper/2312.04653/) (2023)
- [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)
- [The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions](https://mlsanity.com/paper/2605.01756/) (2026)
- [Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization](https://mlsanity.com/paper/2510.19591/) (2025)
- [Profit Maximization in Bilateral Trade against a Smooth Adversary](https://mlsanity.com/paper/2605.12664/) (2026)
- [Is Online Linear Optimization Sufficient for Strategic Robustness?](https://mlsanity.com/paper/2602.12253/) (2026)

## Abstract (arXiv)

We study the problem of regret minimization for a single bidder in a sequence of first-price auctions where the bidder discovers the item's value only if the auction is won. Our main contribution is a complete characterization, up to logarithmic factors, of the minimax regret in terms of the auction's transparency, which controls the amount of information on competing bids disclosed by the auctioneer at the end of each auction. Our results hold under different assumptions (stochastic, adversarial, and their smoothed variants) on the environment generating the bidder's valuations and competing bids. These minimax rates reveal how the interplay between transparency and the nature of the environment affects how fast one can learn to bid optimally in first-price auctions.