Related Work

发表于

1.研究的问题 2. 研究的对象 3. 使用的技术。

AMD:

1.原版

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@misc{conitzer2002complexitymechanismdesign,
      title={Complexity of Mechanism Design}, 
      author={Vincent Conitzer and Tuomas Sandholm},
      year={2002},
      eprint={cs/0205075},
      archivePrefix={arXiv},
      primaryClass={cs.GT},
      url={https://arxiv.org/abs/cs/0205075}, 
}
@inproceedings{conitzer2004self,
  title={Self-interested automated mechanism design and implications for optimal combinatorial auctions},
  author={Conitzer, Vincent and Sandholm, Tuomas},
  booktitle={Proceedings of the 5th ACM Conference on Electronic Commerce},
  pages={132--141},
  year={2004}
}
@inproceedings{sandholm2003automated,
  title={Automated mechanism design: A new application area for search algorithms},
  author={Sandholm, Tuomas},
  booktitle={International Conference on Principles and Practice of Constraint Programming},
  pages={19--36},
  year={2003},
  organization={Springer}
}
@article{sandholm2015automated,
  title={Automated design of revenue-maximizing combinatorial auctions},
  author={Sandholm, Tuomas and Likhodedov, Anton},
  journal={Operations Research},
  volume={63},
  number={5},
  pages={1000--1025},
  year={2015},
  publisher={INFORMS}
}

2.本文研究了在线广告平台中的两阶段拍卖架构，该架构用于在低延迟下向用户传递个性化广告。第一阶段从完整的广告池中高效选择一小部分有潜力的广告。第二阶段在子集中进行拍卖以确定展示的广告，使用第二阶段机器学习模型的点击率预测。研究了第一阶段子集选择策略的在线学习过程，并确保在重复的两阶段广告拍卖中具有博弈论属性。提出了一种新的实验设计，即“集群多重随机化设计”（cMRD），它在客户和搜索查询群集级别独立随机化，允许在单一实验中同时衡量定价变化的直接和间接效果。

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@inproceedings{li2024truthful,
  title={Truthful Bandit Mechanisms for Repeated Two-stage Ad Auctions},
  author={Li, Haoming and Liu, Yumou and Zheng, Zhenzhe and Zhang, Zhilin and Xu, Jian and Wu, Fan},
  booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={1565--1575},
  year={2024}
}

3.regret的一坨

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@article{dutting2024optimal,
  title={Optimal auctions through deep learning: Advances in differentiable economics},
  author={D{\"u}tting, Paul and Feng, Zhe and Narasimhan, Harikrishna and Parkes, David C and Ravindranath, Sai Srivatsa},
  journal={Journal of the ACM},
  volume={71},
  number={1},
  pages={1--53},
  year={2024},
  publisher={ACM New York, NY}
}


衍生

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@inproceedings{duan2022context,
  title={A context-integrated transformer-based neural network for auction design},
  author={Duan, Zhijian and Tang, Jingwu and Yin, Yutong and Feng, Zhe and Yan, Xiang and Zaheer, Manzil and Deng, Xiaotie},
  booktitle={International Conference on Machine Learning},
  pages={5609--5626},
  year={2022},
  organization={PMLR}
}
@article{ivanov2022optimal,
  title={Optimal-er auctions through attention},
  author={Ivanov, Dmitry and Safiulin, Iskander and Filippov, Igor and Balabaeva, Ksenia},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={34734--34747},
  year={2022}
}

  1. “[A Context-Integrated Transformer-Based Neural Network for Auction Design](A Context-Integrated Transformer-Based Neural Network for Auction Design | 一个田螺突然就 (2010727302.github.io))” 介绍了一种基于Transformer模型的神经网络，该网络能够整合上下文信息来设计拍卖机制。这种方法能够更好地适应动态变化的市场环境和参与者的多样性。

    introduces a neural network based on the Transformer model that integrates contextual information to design auction mechanisms. This approach better adapts to the dynamic market environment and the diversity of participants.

  2. “Optimal-er Auctions through Attention” 则专注于利用注意力机制来改进拍卖设计。通过关注关键的拍卖参数和参与者特征，该研究提出了一种能够自动调整拍卖规则以适应不同场景的算法。

    focuses on improving auction design using attention mechanisms. By focusing on key auction parameters and participant features, the study proposes an algorithm that can automatically adjust auction rules to fit different scenarios.

with constraints

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@inproceedings{feng2018deep,
  title={Deep learning for revenue-optimal auctions with budgets},
  author={Feng, Zhe and Narasimhan, Harikrishna and Parkes, David C},
  booktitle={Proceedings of the 17th international conference on autonomous agents and multiagent systems},
  pages={354--362},
  year={2018}
}

4.joint

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@inproceedings{zhang2024joint,
  title={Joint Auction in the Online Advertising Market},
  author={Zhang, Zhen and Li, Weian and Lei, Yahui and Wang, Bingzhe and Zhang, Zhicheng and Qi, Qi and Liu, Qiang and Wang, Xingxing},
  booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={4362--4373},
  year={2024}
}
@article{aggarwal2024selling,
  title={Selling joint ads: A regret minimization perspective},
  author={Aggarwal, Gagan and Badanidiyuru, Ashwinkumar and D{\"u}tting, Paul and Fusco, Federico},
  journal={arXiv preprint arXiv:2409.07819},
  year={2024}
}
@inproceedings{ma2024joint,
  title={Joint Bidding in Ad Auctions},
  author={Ma, Yuchao and Li, Weian and Zhang, Wanzhi and Lei, Yahui and Zhang, Zhicheng and Qi, Qi and Liu, Qiang and Wang, Xingxing},
  booktitle={Annual Conference on Theory and Applications of Models of Computation},
  pages={344--354},
  year={2024},
  organization={Springer}
}

Selling joint ads: A regret minimization perspective