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Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs.
Bennett, S.: Development of the pid controller. IEEE control systems pp. 58–62 (1993)
1993
Earlier work this paper cites.
Konda, V.R., Tsitsiklis, J.N.: Actor-critic algorithms. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 1008–1014 (2000)
2000
Earlier work this paper cites.
Sutton, R.S., McAllester, D.A., Singh, S.P., Mansour, Y.: Policy gradient methods for reinforcement learning with function approximation. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 1057–1063 (2000)
2000
Earlier work this paper cites.
Shelton, C.R.: Balancing multiple sources of reward in reinforcement learning. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 1082–1088 (2001)
2001
Earlier work this paper cites.
Lizotte, D.J., Bowling, M.H., Murphy, S.A.: Efficient reinforcement learning with multiple reward functions for randomized controlled trial analysis. In: International Conference on Machine Learning (ICML). pp. 695–702 (2010)
2010
Earlier work this paper cites.
Perlich, C., Dalessandro, B., Hook, R., Stitelman, O., Raeder, T., Provost, F.: Bid optimizing and inventory scoring in targeted online advertising. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). pp. 804–812 (2012)
2012
Earlier work this paper cites.
Yuan, S., Wang, J., Zhao, X.: Real-time bidding for online advertising: measurement and analysis. In: Proceedings of the 7th International Workshop on Data Mining for Online Advertising (ADKDD). p. 3 (2013)
2013
Earlier work this paper cites.
Zhang, W., Yuan, S., Wang, J.: Optimal real-time bidding for display advertising. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). pp. 1077–1086 (2014)
2014
Earlier work this paper cites.
Ghavamzadeh, M., Mannor, S., Pineau, J., Tamar, A., et al.: Bayesian reinforcement learning: A survey. Foundations and Trends® in Machine Learning 8
2015
Earlier work this paper cites.
2015
Cited alongside, same era.
Pirotta, M., Parisi, S., Restelli, M.: Multi-objective reinforcement learning with continuous pareto frontier approximation. In: 29th AAAI Conference on Artificial Intelligence (AAAI) (2015)
2015
Cited alongside, same era.
Xu, J., Lee, K.c., Li, W., Qi, H., Lu, Q.: Smart pacing for effective online ad campaign optimization. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). pp. 2217–2226 (2015)
2015
Cited alongside, same era.
Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning (ICML). pp. 1928–1937 (2016)
2016
Cited alongside, same era.
2018
Later among the works it cites.
Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 525–536 (2018)
2018
Later among the works it cites.
Wu, D., Chen, X., Yang, X., Wang, H., Tan, Q., Zhang, X., Xu, J., Gai, K.: Budget constrained bidding by model-free reinforcement learning in display advertising. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM) (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
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Cai, H., Ren, K., Zhang, W., Malialis, K., Wang, J., Yu, Y., Guo, D.: Real-time bidding by reinforcement learning in display advertising. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM). pp. 661–670 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Jin, J., Song, C., Li, H., Gai, K., Wang, J., Zhang, W.: Real-time bidding with multi-agent reinforcement learning in display advertising. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM) (2018)
2018
Cited alongside, same era.
A., A., M., D., L., T., N., A., S., D.: Dynamic weights in multi-objective deep reinforcement learning. In: International Conference on Machine Learning (ICML). pp. 11–20 (2019)
2019
Later among the works it cites.
Lin, X., Zhen, H.L., Li, Z., Zhang, Q.F., Kwong, S.: Pareto multi-task learning. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 12060–12070 (2019)
2019
Later among the works it cites.
Lu, J., Yang, C., Gao, X., Wang, L., Li, C., Chen, G.: Reinforcement learning with sequential information clustering in real-time bidding. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM). pp. 1633–1641 (2019)
2019
Later among the works it cites.