1,721,004 research outputs found
Recommended from our members
Reinforcement Learning in Structured and Partially Observable Environments
Full text linkSequentially making-decision abounds in real-world problems ranging from robots needing to interact with humans to companies aiming to provide reasonable services to their customers. It is as diverse as self-driving cars, health-care, agriculture, robotics, manufacturing, drug discovery, and aerospace. Reinforcement Learning (RL), as the study of sequential decision-making under uncertainty, represents a core aspect challenges in real-world applications. While most of the practical application of interests in RL are high dimensions, we study RL problems from theory to practice in high dimensional, structured, and partially observable settings. We show how statistically develop efficient RL algorithm for a variety of RL problems, from recommendation systems to robotics and games. We theoretically study these problems from their first principles to provide RL agents which efficiently interact with their surrounding environment and learn the desired behavior while minimizing their regrets. We study linear bandit problems where we propose Projected Stochastic Linear Bandit (PSLB), upper confidence bound based algorithm in linear bandit which exploit the intrinsic structure of the decision-making problem to significantly enhance the performance of RL agents. We study the problem of RL in Markov Decision Process (MDP) where we propose the first sample efficient model-free algorithm for the general continuous state and action space MDPs. We further investigate safe RL setting and introduce a safe RL algorithm to avoid catastrophic mistakes that can be made by an RL agent. We extensively study tree-based methods, a well-popularized method in RL which is also the core to Alpha-Go, a technique to beat the masters of board games such as Go game. We extend our study to partially observable environments, such as partially observable Markov decision processes (POMDP) where we propose the first regret analysis for the class of memoryless policies. We continue this study to a class of problems known as rich observable Markov decision processes (ROMPD) and propose the first regret bound with no dependency in the ambient dimension in the dominating terms. We empirically study the significance of all these theoretically guaranteed methods and show their value in practice
Competitive Gradient Optimization
Full text linkWe study the problem of convergence to a stationary point in zero-sum games.
We propose competitive gradient optimization (CGO ), a gradient-based method
that incorporates the interactions between the two players in zero-sum games
for optimization updates. We provide continuous-time analysis of CGO and its
convergence properties while showing that in the continuous limit, CGO
predecessors degenerate to their gradient descent ascent (GDA) variants. We
provide a rate of convergence to stationary points and further propose a
generalized class of -coherent function for which we provide
convergence analysis. We show that for strictly -coherent functions,
our algorithm convergences to a saddle point. Moreover, we propose optimistic
CGO (OCGO), an optimistic variant, for which we show convergence rate to saddle
points in -coherent class of functions
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
