[MMV10] Ankur Moitra and Gregory Valiant. Settling the polynomial learnability of mixtures of gaussians. In 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, pages 93-102. IEEE, 2010.
[MMV21] Andrea Montanari and Ramji Venkataramanan. Estimation of low-rank matrices via approximate message passing. The Annals of Statistics, 49(1), 2021.
[MW23] Andrea Montanari and Yuchen Wu. Posterior sampling from the spiked models via diffusion processes. arXiv preprint arXiv:2304.11449, 2023.
[Pea94] Karl Pearson. Contributions to the mathematical theory of evolution. Philosophical Transactions of the Royal Society of London. A, 185:71-110, 1894.
[Pid22] Jakiw Pidstrigach. Score-based generative models detect manifolds. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 35852-35865. Curran Associates, Inc., 2022.
$\left[\mathrm{RBL}^{+}22\right]$  Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022.
$\left[\mathrm{RDN}^{+}22\right]$  Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.
[RV17] Oded Regev and Aravindan Vijayaraghavan. On learning mixtures of well-separated gaussians. In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS), pages 85-96. IEEE, 2017.
[SCS+22] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022.
[SDWMG15] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015.
[SE19] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems, 32, 2019.
[SN21] Nimrod Segol and Boaz Nadler. Improved convergence guarantees for learning gaussian mixture models by em and gradient em. Electronic journal of statistics, 15(2):4510-4544, 2021.
[SOAJ14] Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, and Ashkan Jafarpour. Near-optimal-sample estimators for spherical gaussian mixtures. Advances in Neural Information Processing Systems, 27, 2014.
[SSDK+20] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.
[Ver] Roman Vershynin. High-Dimensional Probability: An Introduction with Applications in Data Science. Number 47 in Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press.
[IVW04] Santosh Vempala and Grant Wang. A spectral algorithm for learning mixture models. Journal of Computer and System Sciences, 68(4):841-860, 2004. Special Issue on FOCS 2002.