Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction-Reference-Cited by-同舟云学术

Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction

Published:2021 Issue: Volume: Page:314-329
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases. Research Track
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Author:

Yue Yun,Liu Yongchao,Tong Suo,Li Minghao,Zhang Zhen,Wen Chunyang,Bao Huanjun,Gu Lihong,Gu Jinjie,Mu Yixiang

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-86523-8_19

Reference29 articles.

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3. Criteo: Criteo display ad challenge (2014). http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset

4. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011). https://doi.org/10.5555/1953048.2021068

5. Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft’s Bing search engine. In: Fürnkranz, J., Joachims, T. (eds.) Proceedings of the 27th International Conference on Machine Learning, ICML 2010, Haifa, Israel, 21–24 June 2010, pp. 13–20. Omnipress (2010). https://icml.cc/Conferences/2010/papers/901.pdf

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