Lichman, M. (2013). UCI machine learning repository.
Liggett, T. M. (2010). *Continuous time Markov processes: an introduction*, volume 113. American Mathematical Soc.
Liu, C., Zhu, L., and Belkin, M. (2022). Loss landscapes and optimization in over-parameterized non-linear systems and neural networks. Applied and Computational Harmonic Analysis, 59:85-116.
Liu, Q. (2017). Stein variational gradient descent as gradient flow. Advances in neural information processing systems, 30.
Liu, Q. and Wang, D. (2016). Stein variational gradient descent: A general purpose Bayesian inference algorithm. Advances in neural information processing systems, 29.
Louizos, C. and Welling, M. (2017). Multiplicative normalizing flows for variational Bayesian neural networks. In International Conference on Machine Learning, pages 2218-2227. PMLR.
Lu, J., Lu, Y., and Nolen, J. (2019). Scaling limit of the stein variational gradient descent: The mean field regime. SIAM Journal on Mathematical Analysis, 51(2):648-671.
Ma, C. and Hernández-Lobato, J. M. (2021). Functional variational inference based on stochastic process generators. Advances in Neural Information Processing Systems, 34:21795-21807.
Ma, C., Li, Y., and Hernández-Lobato, J. M. (2019). Variational implicit processes. In International Conference on Machine Learning, pages 4222-4233. PMLR.
Matsubara, T., Knoblauch, J., Briol, F.-X., and Oates, C. J. (2022). Robust generalised Bayesian inference for intractable likelihoods. Journal of the Royal Statistical Society Series B: Statistical Methodology, 84(3):997-1022.
McAlester, D. A. (1999a). PAC-Bayesian model averaging. In Proceedings of the twelfth annual conference on Computational learning theory, pages 164-170. ACM.
McAlester, D. A. (1999b). Some PAC-Bayesian theorems. Machine Learning, 37(3):355-363.
Mescheder, L., Nowozin, S., and Geiger, A. (2017). Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. In International conference on machine learning, pages 2391-2400. PMLR.
Miller, J. W. and Dunson, D. B. (2019). Robust Bayesian inference via coarsening. Journal of the American Statistical Association, 114(527):1113-1125.
Muandet, K., Fukumizu, K., Sriperumbudur, B., Scholkopf, B., et al. (2017). Kernel mean embedding of distributions: A review and beyond. Foundations and Trends® in Machine Learning, 10(1-2):1-141.
Neal, R. M. (2012). Bayesian learning for neural networks, volume 118. Springer Science & Business Media.
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., and Snoek, J. (2019). Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. Advances in neural information processing systems, 32.
Polyanskiy, Y. and Wu, Y. (2014). Lecture notes on information theory. Lecture Notes for ECE563 (UIUC) and, 6(2012-2016):7.
Rezende, D. and Mohamed, S. (2015). Variational inference with normalizing flows. In International conference on machine learning, pages 1530-1538. PMLR.
Roberts, G. O. and Tweedie, R. L. (1996). Exponential convergence of Langevin distributions and their discrete approximations. Bernoulli, pages 341-363.
Rodriguez-Santana, S., Zaldivar, B., and Hernandez-Lobato, D. (2022). Function-space inference with sparse implicit processes. In International Conference on Machine Learning, pages 18723-18740. PMLR.