# Auction learning as a two-player game

arXiv: https://arxiv.org/abs/2006.05684 · HTML: https://mlsanity.com/paper/2006.05684/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 29 of 29)

- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [Optimal-er Auctions through Attention](https://mlsanity.com/paper/2202.13110/) (2022)
- [A Scalable Neural Network for DSIC Affine Maximizer Auction Design](https://mlsanity.com/paper/2305.12162/) (2023)
- [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021)
- [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022)
- [A Context-Integrated Transformer-Based Neural Network for Auction Design](https://mlsanity.com/paper/2201.12489/) (2022)
- [Learning Revenue-Maximizing Auctions With Differentiable Matching](https://mlsanity.com/paper/2106.07877/) (2021)
- [On the Convergence of No-Regret Learning Dynamics in Time-Varying Games](https://mlsanity.com/paper/2301.11241/) (2023)
- [GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning](https://mlsanity.com/paper/2406.07428/) (2024)
- [Learning to Mitigate AI Collusion on Economic Platforms](https://mlsanity.com/paper/2202.07106/) (2022)
- [Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning](https://mlsanity.com/paper/2306.07106/) (2023)
- [GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing](https://mlsanity.com/paper/2509.14221/) (2025)
- [Automated Deterministic Auction Design with Objective Decomposition](https://mlsanity.com/paper/2402.11904/) (2024)
- [Goal Recognition Design for General Behavioral Agents using Machine Learning](https://mlsanity.com/paper/2404.03054/) (2024)
- [Deep Incentive Design with Differentiable Equilibrium Blocks](https://mlsanity.com/paper/2603.07705/) (2026)
- [Towards Prior-Free Approximately Truthful One-Shot Auction Learning via Differential Privacy](https://mlsanity.com/paper/2104.00159/) (2021)
- [Neural Auctions Compromise Bidder Information](https://mlsanity.com/paper/2303.00116/) (2023)
- [Optimal Automated Market Makers: Differentiable Economics and Strong Duality](https://mlsanity.com/paper/2402.09129/) (2024)
- [Tracking solutions of time-varying variational inequalities](https://mlsanity.com/paper/2406.14059/) (2024)
- [Deep Reinforcement Learning for Sequential Combinatorial Auctions](https://mlsanity.com/paper/2407.08022/) (2024)
- [Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Design](https://mlsanity.com/paper/2601.13489/) (2026)
- [Advancing Ad Auction Realism: Practical Insights & Modeling Implications](https://mlsanity.com/paper/2307.11732/) (2023)
- [Statistically Truthful Auctions via Acceptance Rule](https://mlsanity.com/paper/2405.12016/) (2024)
- [Achieving PAC Guarantees in Mechanism Design through Multi-Armed Bandits](https://mlsanity.com/paper/2412.00345/) (2024)
- [Deep Learning for Double Auction](https://mlsanity.com/paper/2504.05355/) (2025)
- [Universal Representation of Generalized Convex Functions and their Gradients](https://mlsanity.com/paper/2509.04477/) (2025)
- [LLM Active Alignment: A Nash Equilibrium Perspective](https://mlsanity.com/paper/2602.06836/) (2026)
- [Enhancing Affine Maximizer Auctions with Correlation-Aware Payment](https://mlsanity.com/paper/2602.09455/) (2026)
- [Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learning](https://mlsanity.com/paper/2606.10112/) (2026)

## Abstract (arXiv)

Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. While theoretical approaches to the problem have hit some limits, a recent research direction initiated by Duetting et al. (2019) consists in building neural network architectures to find optimal auctions. We propose two conceptual deviations from their approach which result in enhanced performance. First, we use recent results in theoretical auction design (Rubinstein and Weinberg, 2018) to introduce a time-independent Lagrangian. This not only circumvents the need for an expensive hyper-parameter search (as in prior work), but also provides a principled metric to compare the performance of two auctions (absent from prior work). Second, the optimization procedure in previous work uses an inner maximization loop to compute optimal misreports. We amortize this process through the introduction of an additional neural network. We demonstrate the effectiveness of our approach by learning competitive or strictly improved auctions compared to prior work. Both results together further imply a novel formulation of Auction Design as a two-player game with stationary utility functions.