1,720,956 research outputs found
On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation
Full text linkOff-policy methods are the basis of a large number of effective Policy Optimization (PO) algorithms. In this setting, Importance Sampling (IS) is typically employed for off-policy evaluation, with the goal of estimating the performance of a target policy, given samples collected with a different behavioral policy. However, in Monte Carlo simulation, IS represents a variance minimization approach. In this field, a suitable behavioral distribution is employed for sampling, allowing diminishing the variance of the estimator below the one achievable when sampling from the target distribution. In this paper, we analyze IS in these two guises in the context of PO. We provide a novel view of off-policy PO, showing a connection between the policy improvement and variance minimization objectives. Then, we illustrate how minimizing the off-policy variance can, in some circumstances, lead to a policy improvement, with the advantage, compared with direct off-policy learning, of implicitly enforcing a trust region. Finally, we present numerical simulations on continuous RL benchmarks, with a particular focus on the robustness to small batch sizes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
Optimal policy evaluation for policy optimization
Full text linkLAUREA MAGISTRALEUn considerevole numero di algoritmi di Ottimizzazione della Politica si affida, con successo, a metodi off-policy. In tale contesto, la tecnica dell'Importance Sampling è generalmente sfruttata come uno strumento passivo di analisi. Infatti, partendo dall'esperienza generata eseguendo una politica comportamentale, essa consente di stimare la performance di una diversa politica target. Tuttavia, nelle simulazioni Monte Carlo, l'Importance Sampling rappresenta una tecnica di riduzione della varianza. In questo ambito, il campionamento avviene da un'idonea distribuzione comportamentale, permettendo così di diminuire la varianza dello stimatore rispetto ad un campionamento dalla distribuzione target. In questo lavoro di tesi, la duplice natura dell'Importance Sampling viene approfondita, mostrando le relazioni che sussistono tra i due obiettivi. Viene infatti dimostrato che la minimizzazione della varianza può essere utilizzata come strumento di miglioramento della performance. Inoltre, rispetto ad approcci off-policy tradizionali, tale intuizione fornisce il vantaggio aggiuntivo di indurre una regione di confidenza implicita. Tali proprietà teoriche sono tradotte in un nuovo algoritmo di ottimizzazione della politica, detto Policy Optimization via Optimal Policy Evaluation (PO2PE), che impiega la minimizzazione della varianza in un ciclo interno. Infine, sono presentate evidenze sperimentali sui classici benchmark dell'Apprendimento per Rinforzo, con particolare attenzione alla stabilità del motodo, anche a fronte di uno scarso numero di campioni.Off-policy methods are the basis of a large number of effective Policy Optimization algorithms. In this setting, Importance Sampling is typically employed as a what-if analysis tool, with the goal of estimating the performance of a target policy, given samples collected with a different behavioral policy. However, in Monte Carlo simulation, Importance Sampling represents a variance minimization approach. In this field, a suitable behavioral distribution is employed for sampling, allowing diminishing the variance of the estimator below the one achievable when sampling from the target distribution. In this thesis, Importance Sampling is analyzed in these two guises, showing the connections between the two objectives. It is shown that variance minimization can be used as a performance improvement tool, with the advantage, compared with direct off-policy learning, of implicitly enforcing a trust region. These theoretical findings are used to build a novel Policy Optimization algorithm, Policy Optimization via Optimal Policy Evaluation (PO2PE), that employs variance minimization as an inner loop. Finally, empirical evaluations on continuous Reinforcement Learning benchmarks are presented, with a particular focus on the robustness to small batch sizes
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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