mlsanityData to 7 Oct 2026

A Context-Integrated Transformer-Based Neural Network for Auction Design

arXiv 2201.12489 ↗First posted 29 Jan 2022cs.GT cs.LG cs.MAcited by 19

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Abstract (arXiv)

One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. While theoretical approaches have encountered bottlenecks in multi-item auctions, recently, there has been much progress on finding the optimal mechanism through deep learning. However, these works either focus on a fixed set of bidders and items, or restrict the auction to be symmetric. In this work, we overcome such limitations by factoring public contextual information of bidders and items into the auction learning framework. We propose mathttCITransNet, a context-integrated transformer-based neural network for optimal auction design, which maintains permutation-equivariance over bids and contexts while being able to find asymmetric solutions. We show by extensive experiments that mathttCITransNet can recover the known optimal solutions in single-item settings, outperform strong baselines in multi-item auctions, and generalize well to cases other than those in training.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 27 of its 60 references that are in the map. First posted on arXiv 29 Jan 2022.

Builds on

27 of 60 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
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  5. 2016Key-Value Memory Networks for Directly Reading Documentscited by 223
  6. 2015The Sample Complexity of Revenue Maximizationcited by 48
  7. 2020Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctionscited by 34
  8. 2014Complexity of Mechanism Designcited by 33
  9. 2018Automated Mechanism Design via Neural Networkscited by 31
  10. 2020A Permutation-Equivariant Neural Network Architecture For Auction Designcited by 31
  11. 2021Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertisingcited by 31
  12. 2020Auction learning as a two-player gamecited by 29
15 more
  1. 2016Sample Complexity of Automated Mechanism Designcited by 24
  2. 2019Settling the Sample Complexity of Single-parameter Revenue Maximizationcited by 24
  3. 2021PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learningcited by 22
  4. 2020Certifying Strategyproof Auction Networkscited by 21
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  7. 2018The Sample Complexity of Up-to-varepsilon Multi-Dimensional Revenue Maximizationcited by 17
  8. 2014Duality and Optimality of Auctions for Uniform Distributionscited by 12
  9. 2017Reinforcement Mechanism Design, with Applications to Dynamic Pricing in Sponsored Search Auctionscited by 11
  10. 2014A Simple and Approximately Optimal Mechanism for an Additive Buyercited by 9
  11. 2017Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approachcited by 8
  12. 2014An n-to-1 Bidder Reduction for Multi-item Auctions and its Applicationscited by 7
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 5; 2025: 2; 2026: 4.

19 papers.

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  1. 2022Optimal-er Auctions through Attentioncited by 26
  2. 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Designcited by 23
  3. 2022Differentiable Economics for Randomized Affine Maximizer Auctionscited by 21
  4. 2024Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contractscited by 8
  5. 2022Benefits of Permutation-Equivariance in Auction Mechanismscited by 7
  6. 2023Data Market Design through Deep Learningcited by 7
  7. 2023Mode Connectivity in Auction Designcited by 3
  8. 2023Are Equivariant Equilibrium Approximators Beneficial?cited by 2
  9. 2024Automated Deterministic Auction Design with Objective Decompositioncited by 2
  10. 2023Neural Auctions Compromise Bidder Informationcited by 1
  11. 2025Permutation Equivariant Model-based Offline Reinforcement Learning for Auto-biddingcited by 1
  12. 2026Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Designcited by 1
7 more
  1. 2024Large-Scale Contextual Market Equilibrium Computation through Deep Learningcited by 0
  2. 2024Deep Reinforcement Learning for Sequential Combinatorial Auctionscited by 0
  3. 2024Learning Neural Strategy-Proof Matching Mechanism from Examplescited by 0
  4. 2025Deep Learning for Double Auctioncited by 0
  5. 2026Enhancing Affine Maximizer Auctions with Correlation-Aware Paymentcited by 0
  6. 2026Deep Incentive Design with Differentiable Equilibrium Blockscited by 0
  7. 2026MenuNet: A Strategy-Proof Mechanism for Matching Marketscited by 0