[41] H. Lee, J. Lu, and Y. Tan. Convergence of score-based generative modeling for general data distributions. In International Conference on Algorithmic Learning Theory, pages 946-985. PMLR, 2023.
[42] A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martin-Martin. What matters in learning from offline human demonstrations for robot manipulation. arXiv preprint arXiv:2108.03298, 2021.
[43] T. Marcucci, J. Umenberger, P. A. Parrilo, and R. Tedrake. Shortest paths in graphs of convex sets. arXiv preprint arXiv:2101.11565, 2021.
[44] A. Maurer. A vector-contraction inequality for rademacher complexities. In Algorithmic Learning Theory: 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings 27, pages 3-17. Springer, 2016.
[45] M. Mezard and A. Montanari. Information, physics, and computation. Oxford University Press, 2009.
[46] D. Misra. Mish: A self regularized non-monotonic activation function. arXiv preprint arXiv:1908.08681, 2019.
[47] A. Q. Nichol and P. Dhariwal. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, pages 8162–8171. PMLR, 2021.
[48] T. Pearce, T. Rashid, A. Kanervisto, D. Bignell, M. Sun, R. Georgescu, S. V. Macua, S. Z. Tan, I. Momennejad, K. Hofmann, et al. Imitating human behaviour with diffusion models. arXiv preprint arXiv:2301.10677, 2023.
[49] E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville. Film: Visual reasoning with a general conditioning layer. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
[50] D. Pfrommer, T. Zhang, S. Tu, and N. Matni. Tasil: Taylor series imitation learning. Advances in Neural Information Processing Systems, 35:20162-20174, 2022.
[51] D. Pfrommer, M. Simchowitz, T. Westenbroek, N. Matni, and S. Tu. The power of learned locally linear models for nonlinear policy optimization. arXiv preprint arXiv:2305.09619, 2023.
[52] Y. Polyanskiy and Y. Wu. Information Theory: From Coding to Learning. Cambridge University Press, 2022+.
[53] M. Raginsky, A. Rakhlin, and M. Telgarsky. Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis. In Conference on Learning Theory, pages 1674–1703. PMLR, 2017.
[54] A. Rakhlin, K. Sridharan, and A. B. Tsybakov. Empirical entropy, minimax regret and minimax risk. Bernoulli Society for Mathematical Statistics and Probability, 2017.
[55] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.
[56] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234-241. Springer, 2015.
[57] S. Ross and D. Bagnell. Efficient reductions for imitation learning. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 661-668. JMLR Workshop and Conference Proceedings, 2010.
[58] S. Ross, G. Gordon, and D. Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pages 627-635. JMLR Workshop and Conference Proceedings, 2011.
[59] N. M. Shafiullah, Z. Cui, A. A. Altanzaya, and L. Pinto. Behavior transformers: Cloning  $k$  modes with one stone. Advances in neural information processing systems, 35:22955-22968, 2022.