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Optimal Auctions through Deep Learning [article]

Paul Dütting and Zhe Feng and Harikrishna Narasimhan and David C. Parkes and Sai Srivatsa Ravindranath
2020 arXiv   pre-print
In this work, we initiate the exploration of the use of tools from deep learning for the automated design of optimal auctions.  ...  We model an auction as a multi-layer neural network, frame optimal auction design as a constrained learning problem, and show how it can be solved using standard machine learning pipelines.  ...  The auctions learned by the neural network yield revenue close to the optimal.  ... 
arXiv:1706.03459v5 fatcat:t2set6smirajbldj3llrkk2lvi

Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks

MyungJae Shin, Joongheon Kim, Marco Levorato
2019 IEEE Transactions on Vehicular Technology  
Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online.  ...  Numerical results from extensive simulations show that the proposed deep learning-based approach provides effective battery charging control in multi-drone scenarios.  ...  The revenue optimal auction can be configured through a relatively simple deep learning structure, i.e., composed of max/min operations and a loss function shaping the training process. Theorem 1.  ... 
doi:10.1109/tvt.2019.2903144 fatcat:mrku6mck6jgwxnvoukrqn47h5q

Truthful electric vehicle charging via neural-architectural Myerson auction

Haemin Lee, Soyi Jung, Joongheon Kim
2021 ICT Express  
Based on this need, this paper proposes a deep learning-based auction which increases the charging amounts using Myerson auction while preserving truthfulness.  ...  Performance evaluation In this section, we have applied deep learning-based optimal auction for EV charging scheduling and the proposed deep learning based optimal auction compared with SPA as a baseline  ...  ., selecting one CS in order to charge EV) through a deep learning-based Myerson auction mechanism, as conceptually illustrated in Fig. 1 .  ... 
doi:10.1016/j.icte.2021.03.009 fatcat:7jho6ygvnfbg7dvtimtsbee52m

Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising [article]

Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, Yiqing Wang, Dagui Chen, Jian Xu, Fan Wu, Guihai Chen (+1 others)
2021 arXiv   pre-print
We optimize the performance metrics by developing deep models to efficiently extract contexts from auctions, providing rich features for auction design.  ...  In this paper, we design Deep Neural Auctions (DNAs) to enable end-to-end auction learning by proposing a differentiable model to relax the discrete sorting operation, a key component in auctions.  ...  Technology Innovation 2030 -"New Generation Artificial Intelligence" Major Project No. 2018AAA0100905, in part by China NSF grant No. 62025204, 62072303, 61902248, and 61972254, and in part by Alibaba Group through  ... 
arXiv:2106.03593v2 fatcat:7o6z4bq2gbh3fcan2nusgcnmae

Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising [article]

Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang, Miao Xu, Junwei Pan, Chuan Yu, Fan Wu, Jian Xu, Kun Gai
2021 arXiv   pre-print
In this paper, we propose a new mechanism called Deep GSP auction, which leverages deep learning to design new rank score functions within the celebrated GSP auction framework.  ...  The results demonstrated the effectiveness of the Deep GSP auction compared to the state-of-the-art auction mechanisms.  ...  We propose an end-to-end learning based ad auction mechanism, namely Deep GSP auction, towards optimizing multiple performance metrics in a dynamic and game theoretical setting.  ... 
arXiv:2012.02930v2 fatcat:s3jng4wt3nf27fpqkhnqemwclm

Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning [article]

Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Cyril Leung, Chunyan Miao, Qiang Yang
2020 arXiv   pre-print
Furthermore, we present a case study involving optimal auction design using Deep Learning to price fresh data contributed for edge learning.  ...  The performance evaluation shows the revenue maximizing properties of our proposed auction over the benchmark schemes.  ...  Deep Learning-Based Optimal Auction In the following, we describe the Deep Learning-based auction algorithm [15] .  ... 
arXiv:2006.00511v1 fatcat:7psh6ipc35a27k4jkibjlgzkna

Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach [article]

Nguyen Cong Luong, Zehui Xiong, Ping Wang, Dusit Niyato
2017 arXiv   pre-print
In this paper, we develop an optimal auction based on deep learning for the edge resource allocation.  ...  We show the experimental results to confirm the benefits of using the deep learning for deriving the optimal auction for mobile blockchain with high revenue  ...  To optimize the revenue, the authors in [12] proposed to use deep learning, an emerging tool for finding globally optimal solutions, for optimal auctions.  ... 
arXiv:1711.02844v2 fatcat:3xexzbku4jgbpbyz5q4szgy6le

Distributed and Autonomous Aerial Data Collection in Smart City Surveillance Applications [article]

Haemin Lee, Soyi Jung, Joongheon Kim
2021 arXiv   pre-print
For this purpose, this paper designs a Myerson auction-based deep learning algorithm to leverage the UAV's revenue compare to traditional second-price auction while preserving truthfulness.  ...  PERFORMANCE EVALUATION In this section, we have a deep learning-based optimal auction (DLA) algorithm for data collection.  ...  Furthermore, we utilizes deep learning-based framework for solving the Myerson auction-based formulation for optimizing seller's revenue.  ... 
arXiv:2107.11790v1 fatcat:bydoznukrreinaiak2kjdis2ea

A Permutation-Equivariant Neural Network Architecture For Auction Design [article]

Jad Rahme, Samy Jelassi, Joan Bruna, S. Matthew Weinberg
2021 arXiv   pre-print
Building on the success of deep learning, a new approach was recently proposed by Duetting et al. (2019) in which the auction is modeled by a feed-forward neural network and the design problem is framed  ...  as a learning problem.  ...  All these aforementioned differ from ours as we resort to deep learning for finding optimal auctions. Auction design and deep learning.  ... 
arXiv:2003.01497v4 fatcat:vc6gnrpqcjbo5b6eyqzn3ho6di

Functional Optimization Reinforcement Learning for Real-Time Bidding [article]

Changjie Lu, Yining Lu, Naina Bandyopadhyay, Manoj Kumar, Gaurav Gupta
2022 arXiv   pre-print
In this paper, we proposed a multi-agent reinforcement learning architecture for RTB with functional optimization.  ...  have been assigned to each agent, including biased or unbiased win probability, Lagrange multiplier, and click-through rate.  ...  Index Terms-Real-Time Bidding Optimization, Multi-Agents Deep Reinforcement Learning, Functional Optimization. I.  ... 
arXiv:2206.13939v2 fatcat:gdavcqtujjhvbezgjt2cg3npki

A Survey of Online Auction Mechanism Design Using Deep Learning Approaches [article]

Zhanhao Zhang
2021 arXiv   pre-print
In this article, we summarized some common deep learning infrastructures adopted in auction mechanism designs and showed how these architectures are evolving.  ...  With the advancement of computing technology and the bottleneck in theoretical frameworks, researchers are shifting gears towards online auction designs using deep learning approaches.  ...  Conclusion In this article, we have gone through the rough evolving process of deep learning based online auction systems.  ... 
arXiv:2110.06880v1 fatcat:op5iia46xjfrznq7vfynbknkiq

An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions [article]

Tian Zhou, Hao He, Shengjun Pan, Niklas Karlsson, Bharatbhushan Shetty, Brendan Kitts, Djordje Gligorijevic, San Gultekin, Tingyu Mao, Junwei Pan, Jianlong Zhang, Aaron Flores
2021 arXiv   pre-print
In this study, we introduce a novel deep distribution network for optimal bidding in both open (non-censored) and closed (censored) online first-price auctions.  ...  Bid shading was proposed to adjust the bid price intended for second-price auctions, in order to balance cost and winning probability in a first-price auction setup.  ...  13, 17, 27, 28, 30] , to deep distribution network, as the deep distribution network structure, in learning the distribution of minimum winning price .  ... 
arXiv:2107.06650v1 fatcat:nj7eaepo3fajritgqzihf4ifai

Know Your Enemies and Know Yourself in the Real-Time Bidding Function Optimisation

Manxing Du, Alexander I. Cowen-Rivers, Ying Wen, Phu Sakulwongtana, Jun Wang, Mats Brorsson, Radu State
2019 2019 International Conference on Data Mining Workshops (ICDMW)  
Furthermore, we introduce the DASA model as the opponent model into the Mean Field Deep Deterministic Policy Gradients (DDPG) algorithm for each agent to learn the optimal bidding strategy and converge  ...  Real-time bidding (RTB) is a popular method to sell online ad space inventory using real-time auctions to determine which advertiser gets to make the ad impression.  ...  Index Terms-Real-time bidding, Multi-agent, Deep Reinforcement Learning I.  ... 
doi:10.1109/icdmw.2019.00141 dblp:conf/icdm/DuCWSWBS19 fatcat:uoskbd3r4bfvhjrrwesbm7mxkm

Auction learning as a two-player game [article]

Jad Rahme and Samy Jelassi and S. Matthew Weinberg
2021 arXiv   pre-print
Second, the optimization procedure in previous work uses an inner maximization loop to compute optimal misreports. We amortize this process through the introduction of an additional neural network.  ...  We demonstrate the effectiveness of our approach by learning competitive or strictly improved auctions compared to prior work.  ...  While Duetting et al. (2019) is the first paper to design auctions through deep learning, several other paper followed-up this work.  ... 
arXiv:2006.05684v4 fatcat:6xvtu54gfreoteih2noesojvwe

Learning Revenue-Maximizing Auctions With Differentiable Matching [article]

Michael J. Curry and Uro Lyi and Tom Goldstein and John Dickerson
2021 arXiv   pre-print
We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations.  ...  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.  ...  Differentiable optimization and deep learning Recently, there has been broad interest in mixing convex optimization problems with deep learning.  ... 
arXiv:2106.07877v1 fatcat:mqjx7kkmg5fsnhkhhp7unf4xxa
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