# Learning Optimal Reserve Price against Non-myopic Bidders

arXiv: https://arxiv.org/abs/1804.11060 · HTML: https://mlsanity.com/paper/1804.11060/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [Learning in Stackelberg Games with Non-myopic Agents](https://mlsanity.com/paper/2208.09407/) (2022)
- [Is Learning in Games Good for the Learners?](https://mlsanity.com/paper/2305.19496/) (2023)
- [Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions](https://mlsanity.com/paper/2110.03906/) (2021)
- [Learning to Manipulate a Commitment Optimizer](https://mlsanity.com/paper/2302.11829/) (2023)
- [On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms](https://mlsanity.com/paper/2307.07675/) (2023)
- [Learning in Repeated Multi-Unit Pay-As-Bid Auctions](https://mlsanity.com/paper/2307.15193/) (2023)
- [From No-Regret to Strategically Robust Learning in Repeated Auctions](https://mlsanity.com/paper/2601.03853/) (2026)
- [Learning Local Stackelberg Equilibria from Repeated Interactions with a Learning Agent](https://mlsanity.com/paper/2510.22471/) (2025)
- [Differentially Private Online Item Pricing](https://mlsanity.com/paper/2305.11362/) (2023)
- [Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning](https://mlsanity.com/paper/2407.04898/) (2024)

## Abstract (arXiv)

We consider the problem of learning optimal reserve price in repeated auctions against non-myopic bidders, who may bid strategically in order to gain in future rounds even if the single-round auctions are truthful. Previous algorithms, e.g., empirical pricing, do not provide non-trivial regret rounds in this setting in general. We introduce algorithms that obtain small regret against non-myopic bidders either when the market is large, i.e., no bidder appears in a constant fraction of the rounds, or when the bidders are impatient, i.e., they discount future utility by some factor mildly bounded away from one. Our approach carefully controls what information is revealed to each bidder, and builds on techniques from differentially private online learning as well as the recent line of works on jointly differentially private algorithms.