Publication year
2019Author(s)
Publisher
[S.l.] : [S.n.]
In
Proceedings of Machine Learning Research, (2019)Chaudhuri, K.; Sugiyama, M. (ed.), Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019, pp. 777-786ISSN
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Annotation
International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 (Naha, Okinawa, Japan, April 16 - 18, 2019)
Publication type
Article in monograph or in proceedings

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Editor(s)
Chaudhuri, K.
Sugiyama, M.
Organization
SW OZ DCC AI
SW OZ DCC SMN
Journal title
Proceedings of Machine Learning Research
Languages used
English (eng)
Book title
Chaudhuri, K.; Sugiyama, M. (ed.), Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019
Page start
p. 777
Page end
p. 786
Subject
Action, intention, and motor control; Cognitive artificial intelligenceAbstract
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortized variational inference is a likelihood-free method as its gradient can be sampled without bias and without requiring any evaluation of either the model joint distribution or its derivatives. We prove that our new variational loss is optimized by the exact posterior marginals in the fully factorized mean-field approximation, a property that is not shared with the more conventional reverse KL inference. Furthermore, we show that forward amortized inference can be easily marginalized over large families of latent variables in order to obtain a marginalized variational posterior. We consider two examples of variational marginalization. In our first example we train a Bayesian forecaster for predicting a simplified chaotic model of atmospheric convection. In the second example we train an amortized variational approximation of a Bayesian optimal classifier by marginalizing over the model space. The result is a powerful meta-classification network that can solve arbitrary classification problems without further training.
This item appears in the following Collection(s)
- Academic publications [227900]
- Electronic publications [107393]
- Faculty of Social Sciences [28471]
- Open Access publications [76515]
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