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Analysis of institutional authors

Villacampa-Calvo CCorresponding AuthorHernández-Lobato DAuthor

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October 3, 2022
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Alpha-divergence minimization for deep Gaussian processes

Publicated to:INTERNATIONAL JOURNAL OF APPROXIMATE REASONING. 150 139-171 - 2022-11-01 150(), DOI: 10.1016/j.ijar.2022.08.003

Authors: Villacampa-Calvo C; Hernández-Muñoz G; Hernández-Lobato D

Affiliations

Univ Autonoma Madrid, Comp Sci Dept, Escuela Politecn Super, C Francisco Tomas y Valiente 11, Madrid 28049, Spain - Author
Universidad Autónoma de Madrid - Author

Abstract

This paper proposes the minimization of α-divergences for approximate inference in the context of deep Gaussian processes (DGPs). The proposed method can be considered as a generalization of variational inference (VI) and expectation propagation (EP), two previously used methods for approximate inference in DGPs. Both VI and EP are based on the minimization of the Kullback-Leibler divergence. The proposed method is based on a scalable version of power expectation propagation, a method that introduces an extra parameter α that specifies the targeted α-divergence to be optimized. In particular, such a method can recover the VI solution when α→0 and the EP solution when α→1. An exhaustive experimental evaluation shows that the minimization of α-divergences via the proposed method is feasible in DGPs and that choosing intermediate values of the α parameter between 0 and 1 can give better results in some problems. This means that one can improve the results of VI and EP when training DGPs. Importantly, the proposed method allows for stochastic optimization techniques, making it able to address datasets with several millions of instances.

Keywords

?-divergencesapproximate inferenceexpectation propagationvariational inferenceApproximate inferenceDeep gaussian processesExpectation propagationVariational inferenceΑ-divergences

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal INTERNATIONAL JOURNAL OF APPROXIMATE REASONING due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Applied Mathematics.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-08:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 1 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://repositorio.uam.es/handle/10486/704028

    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (VILLACAMPA CALVO, CARLOS) and Last Author (HERNANDEZ LOBATO, DANIEL).

    the author responsible for correspondence tasks has been VILLACAMPA CALVO, CARLOS.