Decision-Aware Conditional GANs for Time Series Data

Author:

Sun He1ORCID,Deng Zhun2ORCID,Chen Hui3ORCID,Parkes David1ORCID

Affiliation:

1. Harvard University, US

2. Columbia University, US

3. MIT, US

Publisher

ACM

Reference30 articles.

1. Martín Arjovsky , Soumith Chintala , and Léon Bottou . 2017 . Wasserstein Generative Adversarial Networks . In Proceedings of the 34th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 70) . PMLR, 214–223. Martín Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein Generative Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 70). PMLR, 214–223.

2. Sanjeev Arora , Rong Ge , Yingyu Liang , Tengyu Ma , and Yi Zhang . 2017 . Generalization and Equilibrium in Generative Adversarial Nets (GANs) . In Proceedings of the 34th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 70) . PMLR, 224–232. Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang. 2017. Generalization and Equilibrium in Generative Adversarial Nets (GANs). In Proceedings of the 34th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 70). PMLR, 224–232.

3. Susan Athey , Guido  W Imbens , Jonas Metzger , and Evan Munro . 2021. Using wasserstein generative adversarial networks for the design of monte carlo simulations. Journal of Econometrics ( 2021 ). Susan Athey, Guido W Imbens, Jonas Metzger, and Evan Munro. 2021. Using wasserstein generative adversarial networks for the design of monte carlo simulations. Journal of Econometrics (2021).

4. TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks

5. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks;Bengio Samy;Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems.,2015

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