[KFL+21] Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser, and Terry Lyons. Neural sdes as infinite-dimensional gans. arXiv preprint arXiv:2102.03657, 2021.
[Kid22] Patrick Kidger. On neural differential equations. arXiv preprint arXiv:2202.02435, 2022.
[KMFL20] Patrick Kidger, James Morrill, James Foster, and Terry Lyons. Neural controlled differential equations for irregular time series. arXiv preprint arXiv:2005.08926, 2020.
[KPH+20] Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. Diffwave: A versatile diffusion model for audio synthesis. In International Conference on Learning Representations, 2020.
[LLJ16] Qiang Liu, Jason Lee, and Michael Jordan. A kernelized stein discrepancy for goodness-of-fit tests. In International conference on machine learning, pages 276-284. PMLR, 2016.
$\left[\mathrm{LSC}^{+}21\right]$  Maud Lemercier, Christopher Salvi, Thomas Cass, Edwin V Bonilla, Theodoros Damoulas, and Terry J Lyons. Siggpde: Scaling sparse gaussian processes on sequential data. In International Conference on Machine Learning, pages 6233-6242. PMLR, 2021.
[LSD+21] Maud Lemercier, Christopher Salvi, Theodoros Damoulas, Edwin Bonilla, and Terry Lyons. Distribution regression for sequential data. In International Conference on Artificial Intelligence and Statistics, pages 3754-3762. PMLR, 2021.
[LSZ15] Yujia Li, Kevin Swersky, and Rich Zemel. Generative moment matching networks. In International conference on machine learning, pages 1718-1727. PMLR, 2015.
[LWCD20] Xuechen Li, Ting-Kam Leonard Wong, Ricky TQ Chen, and David K Duvenaud. Scalable gradients and variational inference for stochastic differential equations. In Symposium on Advances in Approximate Bayesian Inference, pages 1-28. PMLR, 2020.
$\left[\mathrm{LWL}^{+}20\right]$  Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, and Michael Wellman. Generating realistic stock market order streams. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01):727-734, 2020.
[Lyo98] Terry J Lyons. Differential equations driven by rough signals. Revista Matematica Iberoamericana, 14(2):215-310, 1998.
[MKYL22] James Morrill, Patrick Kidger, Lingyi Yang, and Terry Lyons. On the choice of interpolation scheme for neural cdes. Transactions on Machine Learning Research, 2022(9), 2022.
[MS13] Edgar C Merkle and Mark Steyvers. Choosing a strictly proper scoring rule. Decision Analysis, 10(4):292-304, 2013.
[MSK+20] James Morrill, Christopher Salvi, Patrick Kidger, James Foster, and Terry Lyons. Neural rough differential equations for long time series. arXiv preprint arXiv:2009.08295, 2020.
[MSKF21] James Morrill, Christopher Salvi, Patrick Kidger, and James Foster. Neural rough differential equations for long time series. In International Conference on Machine Learning, pages 7829-7838. PMLR, 2021.
[NMK+18] Adamantios Ntakaris, Martin Magris, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis. Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods. Journal of Forecasting, 37(8):852-866, 2018.
$\left[\mathrm{NSSV}^{+}21\right]$  Hao Ni, Lukasz Szpruch, Marc Sabate-Vidales, Baoren Xiao, Magnus Wiese, and Shujian Liao. Sig-wasserstein gans for time series generation. In Proceedings of the Second ACM International Conference on AI in Finance, pages 1-8, 2021.
[NSW+20] Hao Ni, Lukasz Szpruch, Magnus Wiese, Shujian Liao, and Baoren Xiao. Conditional sig-wasserstein gans for time series generation. arXiv preprint arXiv:2006.05421, 2020.
[Opp19] Manfred Opper. Variational inference for stochastic differential equations. Annalen der Physik, 531(3):1800233, 2019.