[64] Manfred Opper and Cédric Archambeau, The variational Gaussian approximation revisited, Neural Computation 21 (2009), no. 3, 786-792.
[65] Felix Otto, The geometry of dissipative evolution equations: the porous medium equation, Comm. Partial Differential Equations 26 (2001), 101-174.
[66] Peter Petersen, Riemannian geometry, vol. 171, Springer, 2006.
[67] Matias Quiroz, David J Nott, and Robert Kohn, Gaussian variational approximations for high-dimensional state space models, Bayesian Analysis 1 (2022), no. 1, 1-28.
[68] Danilo Rezende and Shakir Mohamed, Variational inference with normalizing flows, International Conference on Machine Learning, PMLR, 2015, pp. 1530-1538.
[69] Adil Salim, Anna Korba, and Giulia Luise, The Wasserstein proximal gradient algorithm, Advances in Neural Information Processing Systems (H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, eds.), vol. 33, Curran Associates, Inc., 2020, pp. 12356-12366.
[70] Adil Salim, Lukang Sun, and Peter Richtarik, A convergence theory for SVGD in the population limit under Talagrand's inequality  $T1$ , International Conference on Machine Learning, PMLR, 2022, pp. 19139-19152.
[71] Jiaxin Shi, Chang Liu, and Lester Mackey, Sampling with mirrored Stein operators, International Conference on Learning Representations, 2022.
[72] Jiaxin Shi and Lester Mackey, A Finite-Particle Convergence Rate for Stein Variational Gradient Descent, arXiv preprint arXiv:2211.09721 (2022).
[73] Vladimir Spokoiny, Dimension free non-asymptotic bounds on the accuracy of high dimensional Laplace approximation, arXiv preprint arXiv:2204.11038 (2022).
[74] Weijie Su, Stephen Boyd, and Emmanuel Candes, A differential equation for modeling Nesterov's accelerated gradient method: theory and insights, Advances in neural information processing systems, vol. 27, 2014.
[75] Lukang Sun, Avetik Karagulyan, and Peter Richtarik, Convergence of Stein variational gradient descent under a weaker smoothness condition, International Conference on Artificial Intelligence and Statistics, PMLR, 2023, pp. 3693-3717.
[76] Amirhossein Taghvaei and Prashant Mehta, Accelerated flow for probability distributions, International Conference on Machine Learning, PMLR, 2019, pp. 6076-6085.
[77] Asuka Takatsu, Wasserstein geometry of Gaussian measures, Osaka Journal of Mathematics 48 (2011), no. 4, 1005-1026.
[78] Linda SL Tan and David J Nott, Gaussian variational approximation with sparse precision matrices, Statistics and Computing 28 (2018), 259-275.
[79] Gerald Teschl, Ordinary differential equations and dynamical systems, Graduate Studies in Mathematics 140 (2000), 08854-8019.
[80] Marcin Tomczak, Siddharth Swaroop, and Richard Turner, Efficient low rank Gaussian variational inference for neural networks, Advances in Neural Information Processing Systems 33 (2020), 4610-4622.
[81] Aad W van der Vaart, Asymptotic statistics, vol. 3, Cambridge University Press, 2000.
[82] Jesse van Oostrum, Bures-Wasserstein geometry for positive-definite Hermitian matrices and their trace-one subset, Information Geometry 5 (2022), no. 2, 405-425.
[83] Cedric Villani, Topics in optimal transportation, Graduate Studies in Mathematics 58, American Mathematical Society, 2003.
[84] Dilin Wang, Ziyang Tang, Chandrajit Bajaj, and Qiang Liu, Stein variational gradient descent with matrix-valued kernels, Advances in neural information processing systems 32 (2019).
[85] Yifei Wang, Peng Chen, and Wuchen Li, Projected Wasserstein gradient descent for high-dimensional Bayesian inference, SIAM/ASA Journal on Uncertainty Quantification 10 (2022), no. 4, 1513-1532.
[86] Yifei Wang and Wuchen Li, Accelerated information gradient flow, Journal of Scientific Computing 90 (2022), 1-47.