[12] Jinsung Jeon, Jeonghak Kim, Haryong Song, Seunghyeon Cho, and Noseong Park. Gt-gan: General purpose time series synthesis with generative adversarial networks. Advances in Neural Information Processing Systems, 35:36999-37010, 2022.
[13] Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, and Stefano Ermon. Deep latent state space models for time-series generation. In International Conference on Machine Learning, pages 42625-42643. PMLR, 2023.
[14] Ali Seyfi, Jean-Francois Rajotte, and Raymond Ng. Generating multivariate time series with common source coordinated gan (cosci-gan). Advances in Neural Information Processing Systems, 35:32777-32788, 2022.
[15] Daniel Jarrett, Ioana Bica, and Mihaela van der Schaar. Time-series generation by contrastive imitation. Advances in Neural Information Processing Systems, 34:28968-28982, 2021.
[16] Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Broeck. A semantic loss function for deep learning with symbolic knowledge. In International conference on machine learning, pages 5502-5511. PMLR, 2018.
[17] Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik. Objective-reinforced generative adversarial networks (organ) for sequence generation models. arXiv preprint arXiv:1705.10843, 2017.
[18] Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, and Andrea Passerini. Efficient generation of structured objects with constrained adversarial networks. Advances in neural information processing systems, 33:14663-14674, 2020.
[19] Kuzman Ganchev, Joao Graça, Jennifer Gillenwater, and Ben Taskar. Posterior regularization for structured latent variable models. The Journal of Machine Learning Research, 11:2001-2049, 2010.
[20] Naoya Takeishi and Yoshinobu Kawahara. Knowledge-based regularization in generative modeling. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pages 2390-2396, 2021.
[21] Neha Patki, Roy Wedge, and Kalyan Veeramachaneni. The synthetic data vault. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 399-410. IEEE, 2016.
[22] Jike Wang, Chang-Yu Hsieh, Mingyang Wang, Xiaorui Wang, Zhenxing Wu, Dejun Jiang, Benben Liao, Xujun Zhang, Bo Yang, Qiaojun He, et al. Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning. Nature Machine Intelligence, 3(10):914–922, 2021.
[23] Ruben Rodriguez Torrado, Ahmed Khalifa, Michael Cerny Green, Niels Justesen, Sebastian Risi, and Julian Togelius. Bootstrapping conditional gans for video game level generation. In 2020 IEEE Conference on Games (CoG), pages 41-48. IEEE, 2020.
[24] Yexiang Xue and Willem-Jan van Hoeve. Embedding decision diagrams into generative adversarial networks. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 16th International Conference, CPAIOR 2019, Thessaloniki, Greece, June 4-7, 2019, Proceedings 16, pages 616-632. Springer, 2019.
[25] Nicola De Cao and Thomas Kipf. Molgan: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973, 2018.
[26] Roger D Peng. Advanced statistical computing. Work in progress, page 121, 2018.
[27] Nocedal Jorge and J Wright Stephen. Numerical optimization, 2006.
[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020.
[29] Payal Dhar. The carbon impact of artificial intelligence. Nat. Mach. Intell., 2(8):423-425, 2020.