# Deep Learning for Two-Sided Matching

arXiv: https://arxiv.org/abs/2107.03427 · HTML: https://mlsanity.com/paper/2107.03427/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 64 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Pareto Multi-Task Learning](https://mlsanity.com/paper/1912.12854/) (2019)
- [Equivariance Through Parameter-Sharing](https://mlsanity.com/paper/1702.08389/) (2017)
- [Deep Models of Interactions Across Sets](https://mlsanity.com/paper/1803.02879/) (2018)
- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [Bandit Learning in Decentralized Matching Markets](https://mlsanity.com/paper/2012.07348/) (2020)
- [Complexity of Mechanism Design](https://mlsanity.com/paper/1408.1486/) (2014)
- [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)
- [Beyond log2(T) Regret for Decentralized Bandits in Matching Markets](https://mlsanity.com/paper/2103.07501/) (2021)
- [Certifying Strategyproof Auction Networks](https://mlsanity.com/paper/2006.08742/) (2020)
- [Learning in Multi-Stage Decentralized Matching Markets](https://mlsanity.com/paper/2102.06988/) (2021)
- [Partial Strategyproofness: Relaxing Strategyproofness for the Random Assignment Problem](https://mlsanity.com/paper/1401.3675/) (2014)
- [Hybrid Mechanisms: Trading off Strategyproofness and Efficiency of Random Assignment Mechanisms](https://mlsanity.com/paper/1303.2558/) (2013)
- [Random Matching under Priorities: Stability and No Envy Concepts](https://mlsanity.com/paper/1707.01231/) (2017)

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

- [Optimal-er Auctions through Attention](https://mlsanity.com/paper/2202.13110/) (2022)
- [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)
- [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024)
- [Data Market Design through Deep Learning](https://mlsanity.com/paper/2310.20096/) (2023)
- [LLM-Powered Preference Elicitation in Combinatorial Assignment](https://mlsanity.com/paper/2502.10308/) (2025)
- [The Bandit Whisperer: Communication Learning for Restless Bandits](https://mlsanity.com/paper/2408.05686/) (2024)
- [Automated Deterministic Auction Design with Objective Decomposition](https://mlsanity.com/paper/2402.11904/) (2024)
- [DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddings](https://mlsanity.com/paper/2408.13630/) (2024)
- [Bandit Learning in Matching Markets: Utilitarian and Rawlsian Perspectives](https://mlsanity.com/paper/2412.00301/) (2024)
- [Neural Auctions Compromise Bidder Information](https://mlsanity.com/paper/2303.00116/) (2023)
- [Learning Truthful Mechanisms without Discretization](https://mlsanity.com/paper/2506.22911/) (2025)
- [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)
- [MenuNet: A Strategy-Proof Mechanism for Matching Markets](https://mlsanity.com/paper/2605.03216/) (2026)

## Abstract (arXiv)

We initiate the study of deep learning for the automated design of two-sided matching mechanisms. What is of most interest is to use machine learning to understand the possibility of new tradeoffs between strategy-proofness and stability. These properties cannot be achieved simultaneously, but the efficient frontier is not understood. We introduce novel differentiable surrogates for quantifying ordinal strategy-proofness and stability and use them to train differentiable matching mechanisms that map discrete preferences to valid randomized matchings. We demonstrate that the efficient frontier characterized by these learned mechanisms is substantially better than that achievable through a convex combination of baselines of deferred acceptance (stable and strategy-proof for only one side of the market), top trading cycles (strategy-proof for one side, but not stable), and randomized serial dictatorship (strategy-proof for both sides, but not stable). This gives a new target for economic theory and opens up new possibilities for machine learning pipelines in matching market design.