Ambrogioni, Luca, Umut GΓΌΓ§lΓΌ, Yagmur GΓΌΓ§lΓΌtΓΌrk, Max Hinne, Eric Maris, and Marcel A. J. van Gerven. 2018.
βWasserstein Variational Inference.β In
Proceedings of the 32Nd International Conference on Neural Information Processing Systems, 2478β87. NIPSβ18. USA: Curran Associates Inc.
Arjovsky, Martin, Soumith Chintala, and LΓ©on Bottou. 2017.
βWasserstein Generative Adversarial Networks.β In
International Conference on Machine Learning, 214β23.
Beran, Rudolf. 1977.
βMinimum Hellinger Distance Estimates for Parametric Models.β The Annals of Statistics 5 (3): 445β63.
Bissiri, P. G., C. C. Holmes, and S. G. Walker. 2016.
βA General Framework for Updating Belief Distributions.β Journal of the Royal Statistical Society: Series B (Statistical Methodology) 78 (5): 1103β30.
Blanchet, Jose, Yang Kang, and Karthyek Murthy. 2016.
βRobust Wasserstein Profile Inference and Applications to Machine Learning.β arXiv:1610.05627 [Math, Stat], October.
Blanchet, Jose, Yang Kang, Fan Zhang, and Karthyek Murthy. 2017.
βData-Driven Optimal Cost Selection for Distributionally Robust Optimization.β arXiv:1705.07152 [Stat], May.
Block, Per, Marion Hoffman, Isabel J. Raabe, Jennifer Beam Dowd, Charles Rahal, Ridhi Kashyap, and Melinda C. Mills. 2020.
βSocial Network-Based Distancing Strategies to Flatten the COVID 19 Curve in a Post-Lockdown World.β arXiv:2004.07052 [Physics, q-Bio, Stat], April.
Campbell, Trevor, and Tamara Broderick. 2017.
βAutomated Scalable Bayesian Inference via Hilbert Coresets.β arXiv:1710.05053 [Cs, Stat], October.
Dellaporta, Charita, Jeremias Knoblauch, Theodoros Damoulas, and FranΓ§ois-Xavier Briol. 2022.
βRobust Bayesian Inference for Simulator-Based Models via the MMD Posterior Bootstrap.β arXiv:2202.04744 [Cs, Stat], February.
Fernholz, Luisa Turrin. 1983. von Mises calculus for statistical functionals. Lecture Notes in Statistics 19. New York: Springer.
βββ. 2014.
βStatistical Functionals.β In
Wiley StatsRef: Statistics Reference Online. American Cancer Society.
Fong, Edwin, Simon Lyddon, and Chris Holmes. 2019.
βScalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap.β arXiv:1902.03175 [Cs, Stat], August.
Frogner, Charlie, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, and Tomaso A Poggio. 2015.
βLearning with a Wasserstein Loss.β In
Advances in Neural Information Processing Systems 28, edited by C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, 2053β61. Curran Associates, Inc.
Gibbs, Alison L., and Francis Edward Su. 2002.
βOn Choosing and Bounding Probability Metrics.β International Statistical Review 70 (3): 419β35.
Gulrajani, Ishaan, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. 2017.
βImproved Training of Wasserstein GANs.β arXiv:1704.00028 [Cs, Stat], March.
Guo, Xin, Johnny Hong, Tianyi Lin, and Nan Yang. 2017.
βRelaxed Wasserstein with Applications to GANs.β arXiv:1705.07164 [Cs, Stat], May.
Liu, Huidong, Xianfeng Gu, and Dimitris Samaras. 2018.
βA Two-Step Computation of the Exact GAN Wasserstein Distance.β In
International Conference on Machine Learning, 3159β68.
Liu, Qiang, Jason D Lee, and Michael Jordan. 2016.
βA Kernelized Stein Discrepancy for Goodness-of-Fit Tests.β In
Proceedings of The 33rd International Conference on Machine Learning, 9.
Lyddon, Simon, Stephen Walker, and Chris Holmes. 2018.
βNonparametric Learning from Bayesian Models with Randomized Objective Functions.β In
Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2075β85. NIPSβ18. Red Hook, NY, USA: Curran Associates Inc.
Mahdian, Saied, Jose Blanchet, and Peter Glynn. 2019.
βOptimal Transport Relaxations with Application to Wasserstein GANs.β arXiv:1906.03317 [Cs, Math, Stat], June.
Matsubara, Takuo, Jeremias Knoblauch, FranΓ§ois-Xavier Briol, and Chris J. Oates. 2021.
βRobust Generalised Bayesian Inference for Intractable Likelihoods.β arXiv:2104.07359 [Math, Stat], April.
Ostrovski, Georg, Will Dabney, and Remi Munos. n.d. βAutoregressive Quantile Networks for Generative Modeling,β 10.
Pacchiardi, Lorenzo, and Ritabrata Dutta. 2022.
βGeneralized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators.β arXiv:2104.03889 [Stat], March.
Panaretos, Victor M., and Yoav Zemel. 2019.
βStatistical Aspects of Wasserstein Distances.β Annual Review of Statistics and Its Application 6 (1): 405β31.
Ranganath, Rajesh, Dustin Tran, Jaan Altosaar, and David Blei. 2016.
βOperator Variational Inference.β In
Advances in Neural Information Processing Systems 29, edited by D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, 496β504. Curran Associates, Inc.
Santambrogio, Filippo. 2015.
Optimal Transport for Applied Mathematicians. Edited by Filippo Santambrogio. Progress in Nonlinear Differential Equations and Their Applications. Cham: Springer International Publishing.
Schmon, Sebastian M., Patrick W. Cannon, and Jeremias Knoblauch. 2021.
βGeneralized Posteriors in Approximate Bayesian Computation.β arXiv:2011.08644 [Stat], February.
Solomon, Justin, Fernando de Goes, Gabriel PeyrΓ©, Marco Cuturi, Adrian Butscher, Andy Nguyen, Tao Du, and Leonidas Guibas. 2015.
βConvolutional Wasserstein Distances: Efficient Optimal Transportation on Geometric Domains.β ACM Transactions on Graphics 34 (4): 66:1β11.
Wang, Prince Zizhuang, and William Yang Wang. 2019.
βRiemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling.β In
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 284β94. Minneapolis, Minnesota: Association for Computational Linguistics.
Zhang, Rui, Christian Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, and Lexing Xie. 2020.
βQuantile Propagation for Wasserstein-Approximate Gaussian Processes.β In
Proceedings of NeurIPS 2020.
No comments yet. Why not leave one?