Reinforcement Learning with Sequential Information Clustering in Real-Time Bidding

Author:

Lu Junwei1,Yang Chaoqi1,Gao Xiaofeng1,Wang Liubin2,Li Changcheng2,Chen Guihai1

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

2. Tencent, Shenzhen, China

Funder

the National Key R&D Program of China

the Shanghai Science and Technology Fund

the Huawei Innovation Research Program

the State Key Laboratory of Air Traffic Management System and Technology

the CCF-Huawei Database System Innovation Research Plan

the National Natural Science Foundation of China

the Tencent Social Ads Rhino-Bird Focused Research Program

Publisher

ACM

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Model-Free Nonstationary Reinforcement Learning: Near-Optimal Regret and Applications in Multiagent Reinforcement Learning and Inventory Control;Management Science;2024-05-14

2. Imagine and Imitate: Cost-Effective Bidding under Partially Observable Price Landscapes;Big Data and Cognitive Computing;2024-04-28

3. Toward General Function Approximation in Nonstationary Reinforcement Learning;IEEE Journal on Selected Areas in Information Theory;2024

4. A Survey on Bid Optimization in Real-Time Bidding Display Advertising;ACM Transactions on Knowledge Discovery from Data;2023-12-09

5. Optimal Real-Time Bidding Strategy for Position Auctions in Online Advertising;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

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