Learning Revenue-Maximizing Auctions With Differentiable Matching

• URL: http://arxiv.org/abs/2106.07877v1
• Date: Tue, 15 Jun 2021 04:37:57 GMT
• Title: Learning Revenue-Maximizing Auctions With Differentiable Matching
• Authors: Michael J. Curry and Uro Lyi and Tom Goldstein and John Dickerson
• Abstract summary: 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.
• Score: 50.62088223117716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Selling Joint Ads: A Regret Minimization Perspective [7.288063443108292]
  Motivated by online retail, we consider the problem of selling one item (e.g., an ad slot) to two non-excludable buyers (say, a merchant and a brand) This problem captures, for example, situations where a merchant and a brand bid cooperatively in an auction to advertise a product, and both benefit from the ad being shown. A mechanism collects bids from the two and decides whether to allocate and which payments the two parties should make.
  arXiv  Detail & Related papers  (2024-09-12T07:59:10Z)
• Refined Mechanism Design for Approximately Structured Priors via Active Regression [50.71772232237571]
  We consider the problem of a revenue-maximizing seller with a large number of items for sale to $n$ strategic bidders. It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously difficult to characterize or compute.
  arXiv  Detail & Related papers  (2023-10-11T20:34:17Z)
• AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction [0.0]
  We propose AdaEnsemble: a Sparsely-Gated Mixture-of-Experts architecture that can leverage the strengths of heterogeneous feature interaction experts. AdaEnsemble can adaptively choose the feature interaction depth and find the corresponding SparseMoE stacking layer to exit and compute prediction from. We implement the proposed AdaEnsemble and evaluate its performance on real-world datasets.
  arXiv  Detail & Related papers  (2023-01-06T12:08:15Z)
• Pareto-aware Neural Architecture Generation for Diverse Computational Budgets [94.27982238384847]
  Existing methods often perform an independent architecture search process for each target budget. We propose a Neural Architecture Generator (PNAG) which only needs to be trained once and dynamically produces the optimal architecture for any given budget via inference. Such a joint search algorithm not only greatly reduces the overall search cost but also improves the results.
  arXiv  Detail & Related papers  (2022-10-14T08:30:59Z)
• Differentiable Economics for Randomized Affine Maximizer Auctions [78.08387332417604]
  The ideal auction architecture for differentiable economics would be perfectly strategyproof, support multiple bidders and items, and be rich enough to represent the optimal mechanism. We present an architecture that supports multiple bidders and is perfectly strategyproof, but cannot necessarily represent the optimal mechanism. By using the gradient-based optimization tools of differentiable economics, we can now train lottery AMAs, competing with or outperforming prior approaches in revenue.
  arXiv  Detail & Related papers  (2022-02-06T22:01:21Z)
• Bayesian Embeddings for Few-Shot Open World Recognition [60.39866770427436]
  We extend embedding-based few-shot learning algorithms to the open-world recognition setting. We benchmark our framework on open-world extensions of the common MiniImageNet and TieredImageNet few-shot learning datasets.
  arXiv  Detail & Related papers  (2021-07-29T00:38:47Z)
• Certifying Strategyproof Auction Networks [53.37051312298459]
  We focus on the RegretNet architecture, which can represent auctions with arbitrary numbers of items and participants. We propose ways to explicitly verify strategyproofness under a particular valuation profile using techniques from the neural network verification literature.
  arXiv  Detail & Related papers  (2020-06-15T20:22:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.