# Learning Revenue-Maximizing Auctions With Differentiable Matching

arXiv: https://arxiv.org/abs/2106.07877 · HTML: https://mlsanity.com/paper/2106.07877/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 55 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [Learning Generative Models with Sinkhorn Divergences](https://mlsanity.com/paper/1706.00292/) (2017)
- [Sparse Sinkhorn Attention](https://mlsanity.com/paper/2002.11296/) (2020)
- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [Learning Latent Permutations with Gumbel-Sinkhorn Networks](https://mlsanity.com/paper/1802.08665/) (2018)
- [Differentiable Ranks and Sorting using Optimal Transport](https://mlsanity.com/paper/1905.11885/) (2019)
- [Stochastic Optimization of Sorting Networks via Continuous Relaxations](https://mlsanity.com/paper/1903.08850/) (2019)
- [MIPaaL: Mixed Integer Program as a Layer](https://mlsanity.com/paper/1907.05912/) (2019)
- [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://mlsanity.com/paper/1610.06519/) (2016)
- [The Sample Complexity of Revenue Maximization](https://mlsanity.com/paper/1502.00963/) (2015)
- [Automated Mechanism Design via Neural Networks](https://mlsanity.com/paper/1805.03382/) (2018)
- [A Permutation-Equivariant Neural Network Architecture For Auction Design](https://mlsanity.com/paper/2003.01497/) (2020)
- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [Auction learning as a two-player game](https://mlsanity.com/paper/2006.05684/) (2020)
- [Learning Permutations with Sinkhorn Policy Gradient](https://mlsanity.com/paper/1805.07010/) (2018)
- [Sample Complexity of Automated Mechanism Design](https://mlsanity.com/paper/1606.04145/) (2016)
- [Learning Simple Auctions](https://mlsanity.com/paper/1604.03171/) (2016)
- [PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning](https://mlsanity.com/paper/2106.03215/) (2021)
- [Certifying Strategyproof Auction Networks](https://mlsanity.com/paper/2006.08742/) (2020)
- [ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning](https://mlsanity.com/paper/2010.06398/) (2020)
- [Strong Duality for a Multiple-Good Monopolist](https://mlsanity.com/paper/1409.4150/) (2014)
- [Deep Learning-powered Iterative Combinatorial Auctions](https://mlsanity.com/paper/1907.05771/) (2019)
- [Optimal Auctions with Restricted Allocations](https://mlsanity.com/paper/1606.01610/) (2016)
- [Estimating Approximate Incentive Compatibility](https://mlsanity.com/paper/1902.09413/) (2019)
- [Equilibrium Learning in Combinatorial Auctions: Computing Approximate Bayesian Nash Equilibria via Pseudogradient Dynamics](https://mlsanity.com/paper/2101.11946/) (2021)

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

- [Differentiable Economics for Randomized Affine Maximizer Auctions](https://mlsanity.com/paper/2202.02872/) (2022)
- [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)
- [Data Market Design through Deep Learning](https://mlsanity.com/paper/2310.20096/) (2023)
- [QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints](https://mlsanity.com/paper/2401.06820/) (2024)
- [Mode Connectivity in Auction Design](https://mlsanity.com/paper/2305.11005/) (2023)
- [Automated Deterministic Auction Design with Objective Decomposition](https://mlsanity.com/paper/2402.11904/) (2024)
- [Social Environment Design](https://mlsanity.com/paper/2402.14090/) (2024)
- [DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddings](https://mlsanity.com/paper/2408.13630/) (2024)
- [Neural Auctions Compromise Bidder Information](https://mlsanity.com/paper/2303.00116/) (2023)
- [Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications](https://mlsanity.com/paper/2401.05683/) (2024)
- [Learning Neural Strategy-Proof Matching Mechanism from Examples](https://mlsanity.com/paper/2410.19384/) (2024)
- [Regularized Proportional Fairness Mechanism for Resource Allocation Without Money](https://mlsanity.com/paper/2501.01111/) (2025)
- [Universal Representation of Generalized Convex Functions and their Gradients](https://mlsanity.com/paper/2509.04477/) (2025)

## Abstract (arXiv)

We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations. Our architecture uses the Sinkhorn algorithm to perform a differentiable bipartite matching which allows the network to learn strategyproof revenue-maximizing mechanisms in settings not learnable by the previous RegretNet architecture. In particular, our architecture is able to learn mechanisms in settings without free disposal where each bidder must be allocated exactly some number of items. In experiments, we show our approach successfully recovers multiple known optimal mechanisms and high-revenue, low-regret mechanisms in larger settings where the optimal mechanism is unknown.