mlsanityData to 14 Oct 2026

Learning Revenue-Maximizing Auctions With Differentiable Matching

arXiv 2106.07877 ↗First posted 15 Jun 2021cs.GT cs.LGcited by 14

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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.

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

29 of 55 references.

References · 29

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  4. 2017OptNet: Differentiable Optimization as a Layer in Neural Networkscited by 599
  5. 2019Differentiable Convex Optimization Layerscited by 465
  6. 2017Learning Generative Models with Sinkhorn Divergencescited by 314
  7. 2020Sparse Sinkhorn Attentioncited by 251
  8. 2018Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimizationcited by 227
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17 more
  1. 2016Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problemscited by 73
  2. 2015The Sample Complexity of Revenue Maximizationcited by 48
  3. 2018Automated Mechanism Design via Neural Networkscited by 32
  4. 2020A Permutation-Equivariant Neural Network Architecture For Auction Designcited by 32
  5. 2021Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertisingcited by 31
  6. 2020Auction learning as a two-player gamecited by 29
  7. 2018Learning Permutations with Sinkhorn Policy Gradientcited by 26
  8. 2016Sample Complexity of Automated Mechanism Designcited by 24
  9. 2016Learning Simple Auctionscited by 23
  10. 2021PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learningcited by 22
  11. 2020Certifying Strategyproof Auction Networkscited by 21
  12. 2020ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learningcited by 19
  13. 2014Strong Duality for a Multiple-Good Monopolistcited by 17
  14. 2019Deep Learning-powered Iterative Combinatorial Auctionscited by 12
  15. 2016Optimal Auctions with Restricted Allocationscited by 7
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  17. 2021Equilibrium Learning in Combinatorial Auctions: Computing Approximate Bayesian Nash Equilibria via Pseudogradient Dynamicscited by 1

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Citing papers per year: 2022: 2; 2023: 3; 2024: 7; 2025: 2.

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  1. 2022Differentiable Economics for Randomized Affine Maximizer Auctionscited by 22
  2. 2024GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learningcited by 14
  3. 2022Learning to Mitigate AI Collusion on Economic Platformscited by 10
  4. 2023Data Market Design through Deep Learningcited by 8
  5. 2024QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraintscited by 5
  6. 2023Mode Connectivity in Auction Designcited by 3
  7. 2024Automated Deterministic Auction Design with Objective Decompositioncited by 2
  8. 2024Social Environment Designcited by 2
  9. 2024DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddingscited by 2
  10. 2023Neural Auctions Compromise Bidder Informationcited by 1
  11. 2024Deep Learning Meets Mechanism Design: Key Results and Some Novel Applicationscited by 0
  12. 2024Learning Neural Strategy-Proof Matching Mechanism from Examplescited by 0
2 more
  1. 2025Regularized Proportional Fairness Mechanism for Resource Allocation Without Moneycited by 0
  2. 2025Universal Representation of Generalized Convex Functions and their Gradientscited by 0