1,720,957 research outputs found
ZO-JADE: Zeroth-order Curvature-Aware Multi-Agent Convex Optimization
Get PDFIn this work we address the problem of convex optimization in a multi-agent
setting where the objective is to minimize the mean of local cost functions
whose derivatives are not available (e.g. black-box models). Moreover agents
can only communicate with local neighbors according to a connected network
topology. Zeroth-order (ZO) optimization has recently gained increasing
attention in federated learning and multi-agent scenarios exploiting
finite-difference approximations of the gradient using from (directional
gradient) to (central difference full gradient) evaluations of the cost
functions, where is the dimension of the problem. The contribution of this
work is to extend ZO distributed optimization by estimating the curvature of
the local cost functions via finite-difference approximations. In particular,
we propose a novel algorithm named ZO-JADE, that by adding just one extra
point, i.e. in total, allows to simultaneously estimate the gradient and
the diagonal of the local Hessian, which are then combined via average tracking
consensus to obtain an approximated Jacobi descent. Guarantees of semi-global
exponential stability are established via separation of time-scales. Extensive
numerical experiments on real-world data confirm the efficiency and superiority
of our algorithm with respect to several other distributed zeroth-order methods
available in the literature based on only gradient estimates
Novel Bounds for Incremental Hessian Estimation With Application to Zeroth-Order Federated Learning
Get PDFThe Hessian matrix conveys important information about the curvature, spectrum and partial derivatives of a function, and is required in a variety of tasks. However, computing the exact Hessian is prohibitively expensive for high-dimensional input spaces, and is just impossible in zeroth-order optimization, where the objective function is a black-box of which only input-output pairs are known. In this work we address this relevant problem by providing a rigorous analysis of an Hessian estimator available in the literature, allowing it to be used as a provably accurate replacement of the true Hessian matrix. The Hessian estimator is randomized and incremental, and its computation requires only point function evaluations. We provide non-asymptotic convergence bounds on the estimation error and derive the minimum number of function queries needed to achieve a desired accuracy with arbitrarily high probability. In the second part of the paper we show a practical application of our results, introducing a novel optimization algorithm suitable for non-convex and black-box federated learning. The algorithm only requires clients to evaluate their local functions at certain input points, and builds a sufficiently accurate estimate of the global Hessian matrix in a distributed way. The algorithm exploits inexact cubic regularization to escape saddle points and guarantees convergence with optimal iteration complexity and high probability. Numerical results show that the proposed algorithm outperforms the existing zeroth-order federated algorithms in both convex and non-convex problems. Furthermore, we achieve similar performance to state-of-the-art algorithms for federated convex optimization that use exact gradients and Hessian matrices
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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