mlsanityData to 7 Oct 2026

Optimal-er Auctions through Attention

arXiv 2202.13110 ↗First posted 26 Feb 2022cs.LG cs.GTcited by 26

Not on the map. Why?

Abstract (arXiv)

RegretNet is a recent breakthrough in the automated design of revenue-maximizing auctions. It combines the flexibility of deep learning with the regret-based approach to relax the Incentive Compatibility (IC) constraint (that participants prefer to bid truthfully) in order to approximate optimal auctions. We propose two independent improvements of RegretNet. The first is a neural architecture denoted as RegretFormer that is based on attention layers. The second is a loss function that requires explicit specification of an acceptable IC violation denoted as regret budget. We investigate both modifications in an extensive experimental study that includes settings with constant and inconstant number of items and participants, as well as novel validation procedures tailored to regret-based approaches. We find that RegretFormer consistently outperforms RegretNet in revenue (i.e. is optimal-er) and that our loss function both simplifies hyperparameter tuning and allows to unambiguously control the revenue-regret trade-off by selecting the regret budget.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 42 of its 81 references that are in the map. First posted on arXiv 26 Feb 2022.

Builds on

42 of 81 references.

References · 42

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  5. 2016Layer Normalizationcited by 5,579
  6. 2021Convex optimizationcited by 2,479
  7. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  8. 2020Reformer: The Efficient Transformercited by 1,658
  9. 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
  10. 2017Deep Setscited by 1,394
  11. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,096
  12. 2018Efficient Attention: Attention with Linear Complexitiescited by 366
30 more
  1. 2018Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flowcited by 103
  2. 2018Deep Models of Interactions Across Setscited by 68
  3. 2018Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertisingcited by 65
  4. 2015The Sample Complexity of Revenue Maximizationcited by 48
  5. 2014Complexity of Mechanism Designcited by 33
  6. 2018Automated Mechanism Design via Neural Networkscited by 31
  7. 2020A Permutation-Equivariant Neural Network Architecture For Auction Designcited by 31
  8. 2021Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertisingcited by 31
  9. 2020Auction learning as a two-player gamecited by 29
  10. 2016Sample Complexity of Automated Mechanism Designcited by 24
  11. 2019Settling the Sample Complexity of Single-parameter Revenue Maximizationcited by 24
  12. 2016Learning Simple Auctionscited by 23
  13. 2021PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learningcited by 22
  14. 2022Differentiable Economics for Randomized Affine Maximizer Auctionscited by 21
  15. 2020ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learningcited by 19
  16. 2022A Context-Integrated Transformer-Based Neural Network for Auction Designcited by 19
  17. 2014Strong Duality for a Multiple-Good Monopolistcited by 17
  18. 2018The Sample Complexity of Up-to-varepsilon Multi-Dimensional Revenue Maximizationcited by 17
  19. 2014Making the Most of Your Samplescited by 15
  20. 2021Deep Learning for Two-Sided Matchingcited by 15
  21. 2019Deep Learning-powered Iterative Combinatorial Auctionscited by 12
  22. 2016Efficient Empirical Revenue Maximization in Single-Parameter Auction Environmentscited by 11
  23. 2017A Sample Complexity Measure with Applications to Learning Optimal Auctionscited by 11
  24. 2019Learning to bid in revenue-maximizing auctionscited by 7
  25. 2018Learning Optimal Redistribution Mechanisms through Neural Networkscited by 4
  26. 2019Adversarial learning for revenue-maximizing auctionscited by 4
  27. 2015Optimal Auctions vs. Anonymous Pricingcited by 2
  28. 2018Tight Revenue Gaps among Simple Mechanismscited by 1
  29. 2018Tight Approximation Ratio of Anonymous Pricingcited by 1
  30. 2019Approximation Schemes for a Unit-Demand Buyer with Independent Items via Symmetriescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 8; 2025: 3; 2026: 7.

26 papers.

Sort:

By area:

  1. 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Designcited by 23
  2. 2024GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learningcited by 14
  3. 2023Information Design in Multi-Agent Reinforcement Learningcited by 9
  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. 2023Mediated Multi-Agent Reinforcement Learningcited by 4
  8. 2023Learning Payment-Free Resource Allocation Mechanismscited by 3
  9. 2023Are Equivariant Equilibrium Approximators Beneficial?cited by 2
  10. 2024Automated Deterministic Auction Design with Objective Decompositioncited by 2
  11. 2024Social Environment Designcited by 2
  12. 2023Neural Auctions Compromise Bidder Informationcited by 1
14 more
  1. 2026Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Designcited by 1
  2. 2024Deep Learning Meets Mechanism Design: Key Results and Some Novel Applicationscited by 0
  3. 2024Statistically Truthful Auctions via Acceptance Rulecited by 0
  4. 2024Deep Reinforcement Learning for Sequential Combinatorial Auctionscited by 0
  5. 2024Achieving PAC Guarantees in Mechanism Design through Multi-Armed Banditscited by 0
  6. 2025Regularized Proportional Fairness Mechanism for Resource Allocation Without Moneycited by 0
  7. 2025Optimal Auction Design in the Joint Advertisingcited by 0
  8. 2025Universal Representation of Generalized Convex Functions and their Gradientscited by 0
  9. 2026Open Problems in Differentiable Social Choice: Learning Mechanisms, Decisions, and Alignmentcited by 0
  10. 2026LLM Active Alignment: A Nash Equilibrium Perspectivecited by 0
  11. 2026Enhancing Affine Maximizer Auctions with Correlation-Aware Paymentcited by 0
  12. 2026Deep Incentive Design with Differentiable Equilibrium Blockscited by 0
  13. 2026MenuNet: A Strategy-Proof Mechanism for Matching Marketscited by 0
  14. 2026Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networkscited by 0