30 References [Ho ’21] Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models.Advances in neural information processing systems, 33, 6840-6851 [Song ’20] Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2020, October). Score-Based Generative Modeling through Stochastic Differential Equations. In International Conference on Learning Representations. [Song ’21] Song, Y., Durkan, C., Murray, I., & Ermon, S. (2021). Maximum likelihood training of score-based diffusion models.Advances in Neural Information Processing Systems, 34, 1415-1428. [Lipman ’22] Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., & Le, M. (2022, September). Flow Matching for Generative Modeling. In The Eleventh International Conference on Learning Representations. [Nodozi ’23]Nodozi, I., Yan, C., Khare, M., Halder, A., & Mesbah, A. (2023). Neural Schrödinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly. arXiv preprint arXiv:2307.14442. [Léonard ’13] Léonard, C. (2014). A survey of the Schrödinger problem and some of its connections with optimal transport. Discrete and Continuous Dynamical Systems-Series A, 34(4), 1533-1574. [Chen ’21] Chen, Y., Georgiou, T. T., & Pavon, M. (2021). Stochastic control liaisons: Richard sinkhorn meets gaspard monge on a schrodinger bridge. Siam Review, 63(2), 249-313. [Mikami ’08] Mikami, T. (2008). Optimal transportation problem as stochastic mechanics. Selected Papers on Probability and Statistics, Amer. Math. Soc. Transl. Ser, 2(227), 75-94.
31 References [Pavon & Wakolbinger ’91] Pavon, M., & Wakolbinger, A. (1991). On free energy, stochastic control, and Schrödinger processes. In Modeling, Estimation and Control of Systems with Uncertainty: Proceedings of a Conference held in Sopron, Hungary, September 1990 (pp. 334-348). Boston, MA: Birkhäuser Boston. [Brenier & Benamou 2000] Benamou, J. D., & Brenier, Y. (2000). A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problem. Numerische Mathematik, 84(3), 375-393. [Vargas ’21] Vargas, F., Thodoroff, P., Lamacraft, A., & Lawrence, N. (2021). Solving schrödinger bridges via maximum likelihood. Entropy, 23(9), 1134. [De Bortoli ’21] De Bortoli, V., Thornton, J., Heng, J., & Doucet, A. (2021). Diffusion Schrödinger bridge with applications to score-based generative modeling.Advances in Neural Information Processing Systems, 34, 17695-17709. [Shi ’23] Shi, Y., De Bortoli, V., Campbell, A., & Doucet, A. (2023). Diffusion Schr¥" odinger Bridge Matching. arXiv preprint arXiv:2303.16852. [Peluchetti ’23] Peluchetti, S. (2023). Diffusion Bridge Mixture Transports, Schr¥" odinger Bridge Problems and Generative Modeling. arXiv preprint arXiv:2304.00917. [Chen ’23] Chen, T., Liu, G. H., Tao, M., & Theodorou, E. A. (2023). Deep Momentum Multi-Marginal Schr¥" odinger Bridge. arXiv preprint arXiv:2303.01751. [Koshizuka ’22] Koshizuka, T., & Sato, I. (2022, September). Neural Lagrangian Schr¥"{o} dinger Bridge: Diffusion Modeling for Population Dynamics. In The Eleventh International Conference on Learning Representations.
32 References [Neklyudov ’23] Neklyudov, K., Brekelmans, R., Severo, D., & Makhzani, A. (2023). Action Matching: Learning Stochastic Dynamics from Samples. [Chen ’21] Chen, T., Liu, G. H., & Theodorou, E. (2021, October). Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory. In International Conference on Learning Representations. [Liu ’22] Liu, G. H., Chen, T., So, O., & Theodorou, E. (2022). Deep generalized Schrödinger bridge.Advances in Neural Information Processing Systems, 35, 9374-9388. [Wang ’21] Wang, G., Jiao, Y., Xu, Q., Wang, Y., & Yang, C. (2021, July). Deep generative learning via schrödinger bridge. In International Conference on Machine Learning (pp. 10794-10804). PMLR. [Zhang ’22] Zhang, Q., & Chen, Y. (2022). PATH INTEGRAL SAMPLER: A STOCHASTIC CONTROL APPROACH FOR SAMPLING. Proceedings of Machine Learning Research. [Gushchin. ’22] Gushchin, N., Kolesov, A., Korotin, A., Vetrov, D., & Burnaev, E. (2022). Entropic neural optimal transport via diffusion processes. arXiv preprint arXiv:2211.01156. [Somnath ’23]Somnath, V. R., Pariset, M., Hsieh, Y. P., Martinez, M. R., Krause, A., & Bunne, C. (2023). Aligned Diffusion Schrö dinger Bridges. arXiv preprint arXiv:2302.11419. [Tong ’23] Tong, A., Malkin, N., Huguet, G., Zhang, Y., Rector-Brooks, J., Fatras, K., ... & Bengio, Y. (2023, July). Improving and generalizing flow-based generative models with minibatch optimal transport. In ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems. [Tong ’23] Tong, A., Malkin, N., Fatras, K., Atanackovic, L., Zhang, Y., Huguet, G., ... & Bengio, Y. (2023). Simulation-free Schrödinger bridges via score and flow matching. arXiv preprint arXiv:2307.03672.
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