[60] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015.
[61] J. Song, C. Meng, and S. Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.
[62] Y. Song and S. Ermon. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32, 2019.
[63] Y. Song, S. Garg, J. Shi, and S. Ermon. Sliced score matching: A scalable approach to density and score estimation. In Uncertainty in Artificial Intelligence, pages 574-584. PMLR, 2020.
[64] V. Strassen. The existence of probability measures with given marginals. The Annals of Mathematical Statistics, 36(2):423-439, 1965.
[65] X. Sun, S. Yang, and R. Mangharam. Mega-dagger: Imitation learning with multiple imperfect experts. arXiv preprint arXiv:2303.00638, 2023.
[66] R. Tedrake. Lqr-trees: Feedback motion planning on sparse randomized trees. 2009.
[67] R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos, L. Baker, Y. Du, et al. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239, 2022.
[68] S. Tu, A. Robey, T. Zhang, and N. Matni. On the sample complexity of stability constrained imitation learning. In Learning for Dynamics and Control Conference, pages 180-191. PMLR, 2022.
[69] R. Van Handel. Probability in high dimension. Technical report, PRINCETON UNIV NJ, 2014.
[70] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[71] R. Vershynin. High-dimensional probability: An introduction with applications in data science, volume 47. Cambridge university press, 2018.
[72] C. Villani. Topics in optimal transportation, volume 58. American Mathematical Soc., 2021.
[73] C. Villani et al. Optimal transport: old and new, volume 338. Springer, 2009.
[74] P. Vincent. A connection between score matching and denoising autoencoders. Neural computation, 23(7):1661-1674, 2011.
[75] M. J. Wainwright. High-dimensional statistics: A non-asymptotic viewpoint, volume 48. Cambridge university press, 2019.
[76] T. Westenbroek, M. Simchowitz, M. I. Jordan, and S. S. Sastry. On the stability of nonlinear receding horizon control: a geometric perspective. In 2021 60th IEEE Conference on Decision and Control (CDC), pages 742-749. IEEE, 2021.
[77] Y. Wu and K. He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3-19, 2018.
[78] T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel. Deep imitation learning for complex manipulation tasks from virtual reality teleoperation. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 5628-5635. IEEE, 2018.
[79] S. Zhao and A. Grover. Decision stacks: Flexible reinforcement learning via modular generative models. arXiv preprint arXiv:2306.06253, 2023.
[80] T. Z. Zhao, V. Kumar, S. Levine, and C. Finn. Learning fine-grained bimanual manipulation with low-cost hardware. arXiv preprint arXiv:2304.13705, 2023.