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Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning.
Adaptive stochastic gradient algorithms on riemannian manifolds
Kasai, H., Jawanpuria, P., and Mishra, B. (2019) · 1902
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Information and accuracy attainable in the estimation of statistical parameters
Rao, C. R. (1945) · 1945
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Foundations of Differential Geometry, Vols. 1 and 2
Kobayashi, S. and Nomizu, K. (1969) · 1969
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Maximum likelihood estimation of models for residual covariance in spatial regression
Mardia, K. V. and Marshall, R. J. (1984) · 1984
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Generalized autoregressive conditional heteroskedasticity
Bollerslev, T. (1986) · 1986
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A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects
Longford, N. T. (1987) · 1987
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Bayesian methods for mixtures of experts
Waterhouse, S., MacKay, D., and Robinson, T. (1996) · 1996
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Natural gradient works efficiently in learning
Amari, S. (1998) · 1998
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The geometry of algorithms with orthogonality constraints
Edelman, A., Arias, T., and Smith, A. (1998) · 1998
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An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K. (1999) · 1999
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Online model selection based on the variational bayes
Sato, M. (2001) · 2001
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Optimization algorithms exploiting unitary constraints
Manton, J. H. (2002) · 2002
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Optimization algorithms on matrix manifolds
Absil, P.-A., Mahony, R., and Sepulchre, R. (2009) · 2009
Cited alongside, same era.
Notes on optimization on Stiefel manifolds
Tagare, H. D. (2011) · 2011
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An introduction to manifolds
Tu, L. W. (2011) · 2011
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Stochastic gradient descent on Riemannian manifolds
Bonnabel, S. (2013) · 2013
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Stochastic variational inference
Conjugate-computation variational inference: Converting variational inference in non-conjugate models to inferences in conjugate models
Khan, M. E. and Lin, W. (2017) · 2017
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Constrained stochastic gradient descent: The good practice
Roy, S. K. and Harandi, M. (2017) · 2017
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Variational Bayes with intractable likelihood
Tran, M.-N., Nott, D. J., and Kohn, R. (2017) · 2017
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Riemannian adaptive optimization methods
Bécigneul, G. and Ganea, O.-E. (2018) · 2018
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Sylvester normalizing flows for variational inference
Berg, R. v. d., Hasenclever, L., Tomczak, J. M., and Welling, M. (2018) · 2018
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Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J. (2013) · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
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Manopt, a Matlab toolbox for optimization on manifolds
Boumal, N., Mishra, B., Absil, P.-A., and Sepulchre, R. (2014) · 2014
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Annealed important sampling for models with latent variables
Tran, M.-N., Pitt, M. K., and Kohn, R. (2014) · 2014
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A Riemannian symmetric rank-one trust-region method
Huang, W., Absil, P.-A., and Gallivan, K. A. (2015a)
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A Broyden class of quasi-Newton methods for Riemannian optimization
Huang, W., Gallivan, K. A., and Absil, P.-A. (2015b)
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Cholesky QR-based retraction on the generalized Stiefel manifold
Sato, H. and Aihara, K. (2019) · 2019
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Bayesian deep net GLM and GLMM
Tran, M.-N., Nguyen, N., Nott, D., and Kohn, R. (2019) · 2019
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New insights and perspectives on the natural gradient method
Martens, J. (2020) · 2020
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Manifold optimisation assisted Gaussian variational approximation
Zhou, B., Gao, J., Tran, M.-N., and Gerlach, R. (2020) · 2020
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A practical tutorial on Variational Bayes
Tran, M.-N., Nguyen, T.-N., and Dao, V.-H. (2021) · 2021
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