# Ironing in the Dark

arXiv: https://arxiv.org/abs/1511.06918 · HTML: https://mlsanity.com/paper/1511.06918/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2015.

## Built on by (most cited first, 13 of 13)

- [Semi-Bandit Learning for Monotone Stochastic Optimization](https://mlsanity.com/paper/2312.15427/) (2023)
- [Fast Rate Learning in Stochastic First Price Bidding](https://mlsanity.com/paper/2107.01835/) (2021)
- [Bandit Sequential Posted Pricing via Half-Concavity](https://mlsanity.com/paper/2312.12794/) (2023)
- [New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs](https://mlsanity.com/paper/2302.11700/) (2023)
- [Robust Learning of Optimal Auctions](https://mlsanity.com/paper/2107.06259/) (2021)
- [Multi-Platform Budget Management in Ad Markets with Non-IC Auctions](https://mlsanity.com/paper/2306.07352/) (2023)
- [Aligned Textual Scoring Rules](https://mlsanity.com/paper/2507.06221/) (2025)
- [Online Auction Design Using Distribution-Free Uncertainty Quantification with Applications to E-Commerce](https://mlsanity.com/paper/2405.07038/) (2024)
- [Contextual Learning for Stochastic Optimization](https://mlsanity.com/paper/2505.16829/) (2025)
- [Pricing Query Complexity of Multiplicative Revenue Approximation](https://mlsanity.com/paper/2602.10483/) (2026)
- [Is Online Linear Optimization Sufficient for Strategic Robustness?](https://mlsanity.com/paper/2602.12253/) (2026)
- [On the Learning Curves of Revenue Maximization](https://mlsanity.com/paper/2604.26922/) (2026)
- [Learning to Price with Persuasion](https://mlsanity.com/paper/2608.16699/) (2026)

## Abstract (arXiv)

This paper presents the first polynomial-time algorithm for position and matroid auction environments that learns, from samples from an unknown bounded valuation distribution, an auction with expected revenue arbitrarily close to the maximum possible. In contrast to most previous work, our results apply to arbitrary (not necessarily regular) distributions and the strongest possible benchmark, the Myerson-optimal auction. Learning a near-optimal auction for an irregular distribution is technically challenging because it requires learning the appropriate "ironed intervals," a delicate global property of the distribution.