[88] Jonathan Taylor and Robert J Tibshirani. Statistical learning and selective inference. Proceedings of the National Academy of Sciences, 112(25):7629-7634, 2015. 1, 8
[89] Xiaoying Tian, Nan Bi, and Jonathan Taylor. MAGIC: a general, powerful and tractable method for selective inference. arXiv preprint arXiv:1607.02630, 2016. 8, 31
[90] Xiaoying Tian, Snigdha Panigrahi, Jelena Markovic, Nan Bi, and Jonathan Taylor. Selective sampling after solving a convex problem. arXiv preprint arXiv:1609.05609, 2016. 8, 31
[91] Ryan Tibshirani, Rob Tibshirani, Jonatha Taylor, Joshua Loftus, Stephen Reid, and Jelena Markovic. selectiveInference: Tools for Post-Selection Inference, 2019. 9, 34
[92] Tijmen Tieleman and Geoffrey E Hinton. Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2):26-31, 2012. 9, 30, 31, 34
[93] Alexandre B. Tsybakov. Introduction to Nonparametric Estimation. Springer, New York, NY, 2009. 20, 24
[94] Santosh S. Vempala and Andre Wibisono. Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices. In Proceedings of the 33rd International Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 2019. 21
[95] Cédric Villani. Optimal Transport: Old and New. Springer-Verlag, Berlin, 2008. 23
[96] Martin J Wainwright and Michael I Jordan. Graphical Models, Exponential Families, and Variational Inference. Foundations and Trends in Machine Learning, 1(1-2):1-305, 2008. 1
[97] Dilin Wang, Zhe Zeng, and Qiang Liu. Stein Variational Message Passing for Continuous Graphical Models. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 2018. 1
[98] Xiao Wang, Qi Lei, and Ioannis Panageas. Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev. In Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, 2020. 7
[99] Andre Wibisono. Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem. In Proceedings of the 31st Annual Conference on Learning Theory (COLT 2018), Stockholm, Sweden, 2018. 2, 3, 7, 20
[100] Andre Wibisono. Proximal Langevin Algorithm: Rapid Convergence Under Isoperimetry. arXiv preprint arXiv:1911.01469, 2019. 7
[101] Edwin B Wilson. Probable Inference, the Law of Succession, and Statistical Inference. Journal of the American Statistical Association, 22(158):209-212, 1927. 31
[102] Emilio Zappa, Miranda Holmes-Cerfon, and Jonathan Goodman. Monte Carlo on Manifolds: Sampling Densities and Integrating Functions. Communications on Pure and Applied Mathematics, 71(12):2609-2647, 2018. 7
[103] Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, and Marcelo Pereyra. Wasserstein Control of Mirror Langevin Monte Carlo. In Proceedings of the 33rd Annual Conference on Learning Theory (COLT 2020), Graz, Austria, 2020. 1, 7, 18
[104] Ruqi Zhang, Qiang Liu, and Xin T. Tong. Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, 2022. 1, 7
[105] Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, and Bo Zhang. Message Passing Stein Variational Gradient Descent. In Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, 2018. 1