Y. Yang, R. Luo, M. Li, M. Zhou, W. Zhang, and J. Wang, "Mean field multi-agent reinforcement learning, " arXiv preprint arXiv: 1802.05438, 2018.
R. Gummadi, P. Key, and A. Proutiere, "Repeated auctions under budget constraints: Optimal bidding strategies and equilibria, " in the Eighth Ad Auction Workshop, 2012.
K. Iyer, R. Johari, and M. Sundararajan, "Mean field equilibria of dynamic auctions with learning, " ACM SIGecom Exchanges, vol. 10, no. 3, 2011.
C. Perlich, B. Dalessandro, R. Hook, O. Stitelman, T. Raeder, and F. Provost, "Bid optimizing and inventory scoring in targeted online advertising, " in KDD. ACM, 2012.
W. Zhang, S. Yuan, and J. Wang, "Optimal real-time bidding for display advertising, " in KDD. ACM, 2014.
H. Cai, K. Ren, W. Zhang, K. Malialis, J. Wang, Y. Yu, and D. Guo, "Real-time bidding by reinforcement learning in display advertising, " in WSDM. ACM, 2017.
M. Du, R. Sassioui, G. Varisteas, M. Brorsson, O. Cherkaoui, and R. State, "Improving real-time bidding using a constrained markov decision process, " in ADMA. Springer, 2017.
D. Wu, X. Chen, X. Yang, H. Wang, Q. Tan, X. Zhang, J. Xu, and K. Gai, "Budget constrained bidding by model-free reinforcement learning in display advertising, " in Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 2018, pp. 1443-1451.
J. Jin, C. Song, H. Li, K. Gai, J. Wang, and W. Zhang, "Realtime bidding with multi-agent reinforcement learning in display advertising, " arXiv preprint arXiv: 1802.09756, 2018.
J. Zhao, G. Qiu, Z. Guan, W. Zhao, and X. He, "Deep reinforcement learning for sponsored search real-time bidding, " arXiv preprint arXiv: 1803.00259, 2018.
P. Hernandez-Leal, M. Kaisers, T. Baarslag, and E. M. De Cote, "A survey of learning in multiagent environments: Dealing with non-stationarity, " arXiv preprint arXiv: 1707.09183, 2017.
M. L. Littman, "Markov games as a framework for multiagent reinforcement learning, " in Machine Learning Proceedings 1994. Elsevier, 1994.
S. P. Choi, D.-Y. Yeung, and N. L. Zhang, "An environment model for nonstationary reinforcement learning, " in Advances in neural information processing systems, 2000, pp. 987-993.
J. Hu and M. P. Wellman, "Nash q-learning for general-sum stochastic games, " J. Mach. Learn. Res., vol. 4, pp. 1039-1069, Dec. 2003. [Online]. Available: http://dl.acm.org/citation.cfm? id=945365.964288
K. Iyer, R. Johari, and M. Sundararajan, "Mean field equilibria of dynamic auctions with learning, " Management Science, vol. 60, no. 12, 2014.
K. Ren, J. Qin, L. Zheng, Z. Yang, W. Zhang, L. Qiu, and Y. Yu, "Deep recurrent survival analysis, " AAAI, 2019.
Y. Wang, K. Ren, W. Zhang, J. Wang, and Y. Yu, "Functional bid landscape forecasting for display advertising, " in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2016.
W. C.-H. Wu, M.-Y. Yeh, and M.-S. Chen, "Predicting winning price in real time bidding with censored data, " in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015, pp. 1305-1314.
R. G. Miller Jr, Survival analysis. John Wiley & Sons, 2011, vol. 66.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, "Attention is all you need, " in Advances in Neural Information Processing Systems, 2017.
A. Graves, A.-r. Mohamed, and G. Hinton, "Speech recognition with deep recurrent neural networks, " in Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, 2013.
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, "Deep contextualized word representations, " arXiv preprint arXiv: 1802.05365, 2018.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding, " arXiv preprint arXiv: 1810.04805, 2018.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, "Improving language understanding with unsupervised learning, " Technical report, OpenAI, Tech. Rep., 2018.
J. Hu and M. P. Wellman, "Nash q-learning for general-sum stochastic games, " Journal of machine learning research, vol. 4, no. Nov, pp. 1039-1069, 2003.
M. Huang, R. P. Malhamé, P. E. Caines et al., "Large population stochastic dynamic games: closed-loop mckean-vlasov systems and the nash certainty equivalence principle, " Communications in Information & Systems, vol. 6, no. 3, pp. 221-252, 2006.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, "Continuous control with deep reinforcement learning, " arXiv preprint arXiv: 1509.02971, 2015.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition, " in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
J. L. Ba, J. R. Kiros, and G. E. Hinton, "Layer normalization, " arXiv preprint arXiv: 1607.06450, 2016.
W. A. Knaus, F. E. Harrell, J. Lynn, L. Goldman, R. S. Phillips, A. F. Connors, N. V. Dawson, W. J. Fulkerson, R. M. Califf, N. Desbiens et al., "The support prognostic model: objective estimates of survival for seriously ill hospitalized adults, " Annals of internal medicine, vol. 122, no. 3, pp. 191-203, 1995.
H. J. and A. J. S. , "Neural survival recommender, " in WSDM, 2017. [Online]. Available: http://doi.acm.org/10.1145/3018661. 3018719
W. Zhang, S. Yuan, J. Wang, and X. Shen, "Real-time bidding benchmarking with ipinyou dataset, " arXiv preprint arXiv: 1407.7073, 2014.
W. Zhang, S. Yuan, and J. Wang, "Real-time bidding benchmarking with ipinyou dataset, " CoRR, vol. abs/1407.7073, 2014. [Online]. Available: http://arxiv.org/abs/1407.7073
H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin et al., "Ad click prediction: a view from the trenches, " in KDD. ACM, 2013.
S. Varrette, P. Bouvry, H. Cartiaux, and F. Georgatos, "Management of an academic hpc cluster: The ul experience, " in Proc. of the 2014 Intl. Conf. on High Performance Computing & Simulation (HPCS 2014). Bologna, Italy: IEEE, July 2014.
