Khan, Mohammad Emtiyaz, Babanezhad, Reza, Lin, Wu, Schmidt, Mark, & Sugiyama, Masashi. 2016. Faster Stochastic Variational Inference Using Proximal-Gradient Methods with General Divergence Functions. Pages 319–328 of: Proceedings of the Conference on Uncertainty in Artificial Intelligence. UAI'16. Arlington, Virginia, USA: AUAI Press. (pages 7, 10)
Khan, Mohammad Emtiyaz E, Baque, Pierre, Fleuret, François, & Fua, Pascal. 2015. Kullback-Leibler Proximal Variational Inference. In: Advances in Neural Information Processing Systems, vol. 28. Curran Associates, Inc. (pages 7, 10)
Kim, Kyurae, Oh, Jisu, Gardner, Jacob, Dieng, Adji Bousso, & Kim, Hongseok. 2022. Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. Pages 34802-34816 of: Advances in Neural Information Processing Systems, vol. 35. Curran Associates, Inc. (page 2)
Kim, Kyurae, Wu, Kaiwen, Oh, Jisu, & Gardner, Jacob R. 2023. Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. Pages 16853-16876 of: Proceedings of the International Conference on Machine Learning. PMLR, vol. 202. Honolulu, HI, USA: JMLR. (pages 1, 3, 4, 7, 8, 10, 21, 22, 34, 35, 36)
Kingma, Diederik P., & Ba, Jimmy. 2015. Adam: A Method for Stochastic Optimization. In: Proceedings of the International Conference on Learning Representations. (pages 2, 9, 20)
Kingma, Diederik P., & Welling, Max. 2014 (Apr.). Auto-Encoding Variational Bayes. In: Proceedings of the International Conference on Learning Representations. (page 2)
Kucukelbir, Alp, Tran, Dustin, Ranganath, Rajesh, Gelman, Andrew, & Blei, David M. 2017. Automatic Differentiation Variational Inference. Journal of Machine Learning Research, 18(14), 1-45. (pages 1, 3, 7, 21)
Kunstner, Frederik, Chen, Jacques, Lavington, Jonathan Wilder, & Schmidt, Mark. 2023 (Feb.). Noise Is Not the Main Factor behind the Gap between Sgd and Adam on Transformers, but Sign Descent Might Be. In: Proceedings of the International Conference on Learning Representations. (page 9)
Lambert, Marc, Chewi, Sinho, Bach, Francis, Bonnabel, Silvère, & Rigollet, Philippe. 2022. Variational Inference via Wasserstein Gradient Flows. Pages 14434-14447 of: Advances in Neural Information Processing Systems, vol. 35. Curran Associates, Inc. (page 10)
Leger, Jean-Benoist. 2023 (Jan.). Parametrization Cookbook: A Set of Bijective Parametrizations for Using Machine Learning Methods in Statistical Inference. arXiv Preprint arXiv:2301.08297.arXiv. (page 3)
Lin, Wu, Khan, Mohammad Emtiyaz, & Schmidt, Mark. 2019. Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-Family Approximations. Pages 3992-4002 of: Proceedings of the International Conference on Machine Learning. PMLR, vol. 97. JMLR. (page 10)
Liu, Sifan, & Owen, Art B. 2021. Quasi-Monte Carlo Quasi-Newton in Variational Bayes. Journal of Machine Learning Research, 22(243), 1-23. (pages 4, 10)
Magnusson, Måns, Bürkner, Paul, & Vehtari, Aki. 2022 (Nov.). Posteriordb: A Set of Posteriors for Bayesian Inference and Probabilistic Programming. (page 40)
Mishchenko, Konstantin, Khaled, Ahmed, & Richtarik, Peter. 2020. Random Reshuffling: Simple Analysis with Vast Improvements. Pages 17309-17320 of: Advances in Neural Information Processing Systems, vol. 33. Curran Associates, Inc. (page 10)
Naesseth, Christian, Lindsten, Fredrik, & Blei, David. 2020. Markovian Score Climbing: Variational Inference with KL(p||q). Pages 15499-15510 of: Advances in Neural Information Processing Systems, vol. 33. Curran Associates, Inc. (page 2)
Nagaraj, Dheeraj, Jain, Prateek, & Netrapalli, Praneeth. 2019. SGD without Replacement: Sharper Rates for General Smooth Convex Functions. Pages 4703-4711 of: Proceedings of the International Conference on Machine Learning. PMLR, vol. 97. JMLR. (page 10)
Nemirovski, A., Juditsky, A., Lan, G., & Shapiro, A. 2009. Robust Stochastic Approximation Approach to Stochastic Programming. SIAM Journal on Optimization, 19(4), 1574-1609. (page 2)
Nguyen, Lam, Nguyen, Phuong Ha, van Dijk, Marten, Richtarik, Peter, Scheinberg, Katya, & Takac, Martin. 2018. SGD and Hogwild! Convergence without the Bounded Gradients Assumption.