-
2
-
-
77952563025
-
Variational inference for large-scale models of discrete choice
-
M. Braun and J. McAuliffe. Variational inference for large-scale models of discrete choice. Journal of the American Statistical Association, 105(489): 324-335, 2010.
-
(2010)
Journal of the American Statistical Association
, vol.105
, Issue.489
, pp. 324-335
-
-
Braun, M.1
McAuliffe, J.2
-
4
-
-
19844373567
-
A globally convergent linearly constrained lagrangian method for nonlinear optimization
-
Michael P Friedlander and Michael A Saunders. A globally convergent linearly constrained lagrangian method for nonlinear optimization. SIAM Journal on Optimization, 15(3): 863-897, 2005.
-
(2005)
SIAM Journal on Optimization
, vol.15
, Issue.3
, pp. 863-897
-
-
Friedlander, M.P.1
Saunders, M.A.2
-
5
-
-
84878919168
-
Stochastic variational inference
-
M. Hoffman, D. Blei, C. Wang, and J. Paisley. Stochastic variational inference. Journal of Machine Learning Research, 14: 1303-1347, 2013.
-
(2013)
Journal of Machine Learning Research
, vol.14
, pp. 1303-1347
-
-
Hoffman, M.1
Blei, D.2
Wang, C.3
Paisley, J.4
-
6
-
-
79551487646
-
Approximate riemannian conjugate gradient learning for fixed-form variational bayes
-
A. Honkela, T. Raiko, M. Kuusela, M. Tornio, and J. Karhunen. Approximate Riemannian conjugate gradient learning for fixed-form variational Bayes. Journal of Machine Learning Research, 11: 3235-3268, 2011.
-
(2011)
Journal of Machine Learning Research
, vol.11
, pp. 3235-3268
-
-
Honkela, A.1
Raiko, T.2
Kuusela, M.3
Tornio, M.4
Karhunen, J.5
-
10
-
-
84937822896
-
Fast dual variational inference for non-conjugate latent Gaussian models
-
JMLR.org
-
Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, and Matthias Seeger. Fast dual variational inference for non-conjugate latent gaussian models. In ICML (3), Volume 28 of JMLR Proceedings, pages 951-959. JMLR.org, 2013.
-
(2013)
ICML (3), Volume 28 of JMLR Proceedings
, pp. 951-959
-
-
Khan, M.E.1
Aravkin, A.Y.2
Friedlander, M.P.3
Seeger, M.4
-
17
-
-
63249135864
-
The variational Gaussian approximation revisited
-
M. Opper and C. Archambeau. The variational gaussian approximation revisited. Neural Computation, 21(3): 786-792, 2009.
-
(2009)
Neural Computation
, vol.21
, Issue.3
, pp. 786-792
-
-
Opper, M.1
Archambeau, C.2
-
19
-
-
0000061021
-
A quadratically-convergent algorithm for general nonlinear programming problems
-
Stephen M Robinson. A quadratically-convergent algorithm for general nonlinear programming problems. Mathematical programming, 3(1): 145-156, 1972.
-
(1972)
Mathematical Programming
, vol.3
, Issue.1
, pp. 145-156
-
-
Robinson, S.M.1
-
21
-
-
44649181578
-
Bayesian inference and optimal design in the sparse linear model
-
M. Seeger. Bayesian Inference and Optimal Design in the Sparse Linear Model. Journal of Machine Learning Research, 9: 759-813, 2008.
-
(2008)
Journal of Machine Learning Research
, vol.9
, pp. 759-813
-
-
Seeger, M.1
-
22
-
-
74349098311
-
Sparse linear models: Variational approximate inference and Bayesian experimental design
-
M. Seeger. Sparse linear models: Variational approximate inference and Bayesian experimental design. Journal of Physics: Conference Series, 197(012001), 2009.
-
(2009)
Journal of Physics: Conference Series
, vol.197
-
-
Seeger, M.1
-
23
-
-
84856673666
-
Large scale Bayesian inference and experimental design for sparse linear models
-
M. Seeger and H. Nickisch. Large scale Bayesian inference and experimental design for sparse linear models. SIAM Journal of Imaging Sciences, 4(1): 166-199, 2011.
-
(2011)
SIAM Journal of Imaging Sciences
, vol.4
, Issue.1
, pp. 166-199
-
-
Seeger, M.1
Nickisch, H.2
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