Incentive-Aware Learning for Large Markets

Epasto, Alessandro; Mahdian, Mohammad; Mirrokni, Vahab; Zuo, Song

doi:10.1145/3178876.3186042

Cited by 15 publications

(15 citation statements)

References 20 publications

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“…This is a highly heterogeneous set of auctions, and to optimize any of the auction parameters, one needs to cluster this set into smaller, more homogeneous, clusters. However, to ensure that no advertiser can manipulate this process, it is crucial that no advertiser has a large market share in any cluster (see [12] for a theoretical justification of this statement). Hence, keywords must be clustered such that no advertiser is over-represented in any cluster.…”

Section: Introductionmentioning

confidence: 99%

Clustering without Over-Representation

Ahmadian

Epasto

Kumar

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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In this paper we consider clustering problems in which each point is endowed with a color. The goal is to cluster the points to minimize the classical clustering cost but with the additional constraint that no color is over-represented in any cluster. This problem is motivated by practical clustering settings, e.g., in clustering news articles where the color of an article is its source, it is preferable that no single news source dominates any cluster.For the most general version of this problem, we obtain an algorithm that has provable guarantees of performance; our algorithm is based on finding a fractional solution using a linear program and rounding the solution subsequently. For the special case of the problem where no color has an absolute majority in any cluster, we obtain a simpler combinatorial algorithm also with provable guarantees. Experiments on real-world data shows that our algorithms are effective in finding good clustering without over-representation. CCS CONCEPTS• Information systems → Clustering; Data mining; • Theory of computation → Facility location and clustering; Unsupervised learning and clustering. ACM Reference Format:

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Section: Introductionmentioning

confidence: 99%

Clustering without Over-Representation

Ahmadian

Epasto

Kumar

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…Hartline and Roughgarden [2009], Shen and Tang [2017] and Bachrach et al [2014] provide mechanisms that can tradeoff among different objectives. There is also a rich literature on multi-item auctions [Cai et al, 2012;Daskalakis et al, 2013;Wang and Tang, 2014;Yao, 2014;Tang and Wang, 2017;Yao, 2017], and on repeated auctions motivated by online advertising [Amin et al, 2013;Amin et al, 2014;Kanoria and Nazerzadeh, 2014;Balseiro et al, 2017;Epasto et al, 2018;Tang and Zeng, 2018].…”

Section: Related Workmentioning

confidence: 99%

Ex-post IR Dynamic Auctions with Cost-per-Action Payments

Shen

Wang

Zuo

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Motivated by online ad auctions, we consider a repeated auction between one seller and many buyers, where each buyer only has an estimation of her value in each period until she actually receives the item in that period. The seller is allowed to conduct a dynamic auction but must guarantee ex-post individual rationality. In this paper, we use a structure that we call credit accounts to enable a general reduction from any incentive compatible and ex-ante individual rational dynamic auction to an approximate incentive compatible and ex-post individually rational dynamic auction with credit accounts. Our reduction obtains stronger individual rationality guarantees at the cost of weaker incentive compatibility. Surprisingly, our reduction works without any common knowledge assumption. Finally, as a complement to our reduction, we prove that there is no non-trivial auction that is exactly incentive compatible and ex-post individually rational under this setting.

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“…All of these works assume that bidders are myopic: they can strategize within individual auctions, but do not consider the impact of their bids on future auctions. Robustness to non-myopic has recently been studied in (Kanoria and Nazerzadeh, 2014;Epasto et al, 2018). A common idea is to limit the influence of any particular bidder either by assuming a large number of bidders, or imposing a cost associated with manipulation.…”

Section: Introductionmentioning

confidence: 99%

Robust Clearing Price Mechanisms for Reserve Price Optimization

Feng,

Lahaie

2021

Preprint

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Setting an effective reserve price for strategic bidders in repeated auctions is a central question in online advertising. In this paper, we investigate how to set an anonymous reserve price in repeated auctions based on historical bids in a way that balances revenue and incentives to misreport. We propose two simple and computationally efficient methods to set reserve prices based on the notion of a clearing price and make them robust to bidder misreports. The first approach adds random noise to the reserve price, drawing on techniques from differential privacy. The second method applies a smoothing technique by adding noise to the training bids used to compute the reserve price. We provide theoretical guarantees on the trade-offs between the revenue performance and bid-shading incentives of these two mechanisms. Finally, we empirically evaluate our mechanisms on synthetic data to validate our theoretical findings.

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Incentive-Aware Learning for Large Markets

Cited by 15 publications

References 20 publications

Clustering without Over-Representation

Clustering without Over-Representation

Ex-post IR Dynamic Auctions with Cost-per-Action Payments

Robust Clearing Price Mechanisms for Reserve Price Optimization

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