[54] Jason D Lee, Dennis L Sun, Yuekai Sun, and Jonathan E Taylor. Exact post-selection inference, with application to the lasso. The Annals of Statistics, 44(3):907-927, 2016. 1, 8
[55] Yin Tat Lee and Santosh S Vempala. Convergence Rate of Riemannian Hamiltonian Monte Carlo and Faster Polytope Volume Computation. In Proceedings of the 50th Annual ACM Symposium on the Theory of Computing (STOC 2018), pages 1115-1121, Los Angeles, CA, 2018. 7
[56] Tony Lelievre, Mathias Rouset, and Gabriel Stoltz. Free Energy Computations. Imperial College Press, 2010. 7
[57] Tony Lelievre, Mathias Rousset, and Gabriel Stoltz. Hybrid Monte Carlo methods for sampling probability measures on submanifolds. Numerische Mathematik, 143(2):379-421, 2019. 7
[58] Tony Lelievre and Gabriel Stoltz. Partial differential equations and stochastic methods in molecular dynamics. Acta Numerica, 25:681-880, 2016. 7
[59] Tony Lelievre, Gabriel Stoltz, and Wei Zhang. Multiple projection Markov chain Monte Carlo algorithms on submanifolds. IMA Journal of Numerical Analysis, 43(2):737-788, 2023. 7
[60] Lingxiao Li, Qiang Liu, Anna Korba, Mikhail Yurochkin, and Justin Solomon. Sampling with Mollified Interaction Energy Descent. In Proceedings of the 11th International Conference on Learning Representations, Kigali, Rwanda, 2023. 1, 2, 6, 7, 10, 30, 32, 34
[61] Ruilin Li, Molei Tao, Santosh S. Vempala, and Andre Wibisono. The Mirror Langevin Algorithm Converges with Vanishing Bias. In Proceedings of the 33rd International Conference on Algorithmic Learning Theory (ALT 2022), Paris, France, 2022. 1, 7, 18
[62] Qiang Liu, Jason D. Lee, and Michael Jordan. A Kernelized Stein Discrepancy for Goodness-of-fit Tests. In Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), New York, NY, 2016. 26, 27
[63] Qiang Liu and Dilin Wang. Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. In Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, 2016. 1, 3, 7, 30
[64] Suyun Liu and Luis Nunes Vicente. Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach. Computational Management Science, 19(3):513-537, 2022. 10, 32
[65] Xingchao Liu, Xin T. Tong, and Qiang Liu. Sampling with Trustworthy Constraints: A Variational Gradient Framework. In Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS 2021), Online, 2021. 1, 10, 32
[66] Yang Liu, Prajit Ramachandran, Qiang Liu, and Jian Peng. Stein Variational Policy Gradient. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 2017), Sydney, Australia, 2017. 1
[67] Yi-An Ma, Niladri S Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L Bartlett, and Michael I Jordan. Is there an analog of Nesterov acceleration for gradient-based MCMC? Bernoulli, 27(3):1942-1992, 2021. 7
[68] James Martin, Lucas C Wilcox, Carsten Burstedde, and Omar Ghattas. A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion. SIAM Journal on Scientific Computing, 34(3):1460-1487, 2012. 7
[69] Natalia Martinez, Martin Bertran, and Guillermo Sapiro. Minimax Pareto Fairness: A Multi Objective Perspective. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), Online, 2020. 10
[70] Francesco Orabona and Ashok Cutkosky. Tutorial on Parameter-Free Online Learning. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), Online, 2020. 5