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Analysis of institutional authors

Garrido-Merchán EAuthorHernández-Lobato DAuthor

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December 3, 2019
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Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes

Publicated to:NEUROCOMPUTING. 380 20-35 - 2020-03-07 380(), DOI: 10.1016/j.neucom.2019.11.004

Authors: Garrido-Merchán E; Hernández-Lobato D

Affiliations

Universidad Autónoma de Madrid - Author

Abstract

© 2019 Elsevier B.V. Some optimization problems are characterized by an objective that is very expensive, that lacks an analytical expression, and whose evaluations can be contaminated by noise. Bayesian Optimization (BO) methods can be used to solve these problems efficiently. BO relies on a probabilistic model of the objective, which is typically a Gaussian process (GP). This model is used to compute an acquisition function that estimates the expected utility (for solving the optimization problem) of evaluating the objective at each potential new point. A problem with GPs is, however, that they assume real-valued input variables and cannot easily deal with categorical or integer-valued values. Common methods to account for these variables, before evaluating the objective, include assuming they are real and then using a one-hot encoding, for categorical variables, or rounding to the closest integer, for integer-valued variables. We show that this leads to suboptimal results and introduce a novel approach to tackle categorical or integer-valued input variables within the context of BO with GPs. Several synthetic and real-world experiments support our hypotheses and show that our approach outperforms the results of standard BO using GPs on problems with categorical or integer-valued input variables.

Keywords

Bayesian optimizationCategorical variablesGaussian processesInteger-valued variablesParameter tuning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal NEUROCOMPUTING due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position 30/140, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 4.34. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 7.5 (source consulted: FECYT Feb 2024)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-30, the following number of citations:

  • WoS: 78
  • Scopus: 153

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 2025-07-30:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 216.
  • 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: 223 (PlumX).

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

  • The Total Score from Altmetric: 2.25.
  • The number of mentions on the social network X (formerly Twitter): 7 (Altmetric).

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/10486/710149

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 (GARRIDO MERCHAN, EDUARDO CESAR) and Last Author (HERNANDEZ LOBATO, DANIEL).