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Stat319 : Papers

Here is a list of papers, from which you are required to choose a paper to present (more papers will be added if needed).

Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S. and Poole, B., 2020. Score-based generative modeling through stochastic differential equations, 2020

Reeves, G. and Pfister, H.D., Information-Theoretic Proofs for Diffusion Sampling 2025

Benton, J., Shi, Y., De Bortoli, V., Deligiannidis, G. and Doucet, A., From denoising diffusions to denoising markov models, Journal of the Royal Statistical Society Series B: Statistical Methodology, 86(2), pp.286-301, 2024

Montanari, A., Sampling, diffusions, and stochastic localization, Statistical Science (to appear) 2023.

Ho, J. and Salimans, T., Classifier-free diffusion guidance, arXiv preprint arXiv:2207.12598, 2022.

Liu, X., Gong, C. and Liu, Q., Flow straight and fast: Learning to generate and transfer data with rectified flow, arXiv preprint arXiv:2209.03003, 2022.

Lipman, Y., Chen, R.T., Ben-Hamu, H., Nickel, M. and Le, M., Flow matching for generative modeling, arXiv preprint arXiv:2210.02747, 2022.

Albergo, M.S. and Vanden-Eijnden, E., Building normalizing flows with stochastic interpolants, arXiv preprint arXiv:2209.15571, 2022.

Rout, L., Chen, Y., Ruiz, N., Caramanis, C., Shakkottai, S. and Chu, W.S., Semantic image inversion and editing using rectified stochastic differential equations, arXiv preprint arXiv:2410.10792, 2024.

Chen, Y. and Eldan, R., Localization schemes: A framework for proving mixing bounds for Markov chains, In 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), pp. 110–122, IEEE, 2022.

Anari, N., Baronio, C., Chen, C.J., Haqi, A., Koehler, F., Li, A. and Vuong, T.D., Parallel sampling via autospeculation, arXiv preprint arXiv:2511.07869, 2025.

Anari, N., Gao, R. and Rubinstein, A., Parallel sampling via counting, In Proceedings of the 56th Annual ACM Symposium on Theory of Computing, pp. 537–548, 2024.

Chen, S., Chewi, S., Lee, H., Li, Y., Lu, J. and Salim, A., The probability flow ODE is provably fast, Advances in Neural Information Processing Systems, 36, pp. 68552–68575, 2023.

Zhang, M.S., Huan, S., Huang, J., Boffi, N.M., Chen, S. and Chewi, S., Sublinear iterations can suffice even for DDPMs, arXiv preprint arXiv:2511.04844, 2025.

Shi, J., Han, K., Wang, Z., Doucet, A. and Titsias, M., Simplified and generalized masked diffusion for discrete data, Advances in Neural Information Processing Systems, 37, pp. 103131–103167, 2024.

Albergo, M.S., Boffi, N.M. and Vanden-Eijnden, E., Stochastic interpolants: A unifying framework for flows and diffusions, arXiv preprint arXiv:2303.08797, 2023.