Institution
University of Minnesota
Education•Minneapolis, Minnesota, United States•
About: University of Minnesota is a education organization based out in Minneapolis, Minnesota, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 117432 authors who have published 257986 publications receiving 11944239 citations. The organization is also known as: University of Minnesota, Twin Cities & University of Minnesota-Twin Cities.
Topics: Population, Transplantation, Poison control, Health care, Cancer
Papers published on a yearly basis
Papers
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TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
9,873 citations
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TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
9,627 citations
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TL;DR: In this paper, a rank-based data augmentation technique is proposed for estimating the number of missing studies that might exist in a meta-analysis and the effect that these studies might have had on its outcome.
Abstract: We study recently developed nonparametric methods for estimating the number of missing studies that might exist in a meta-analysis and the effect that these studies might have had on its outcome. These are simple rank-based data augmentation techniques, which formalize the use of funnel plots. We show that they provide effective and relatively powerful tests for evaluating the existence of such publication bias. After adjusting for missing studies, we find that the point estimate of the overall effect size is approximately correct and coverage of the effect size confidence intervals is substantially improved, in many cases recovering the nominal confidence levels entirely. We illustrate the trim and fill method on existing meta-analyses of studies in clinical trials and psychometrics.
9,163 citations
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TL;DR: In this paper, results from searches for the standard model Higgs boson in proton-proton collisions at 7 and 8 TeV in the CMS experiment at the LHC, using data samples corresponding to integrated luminosities of up to 5.8 standard deviations.
8,857 citations
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Stockholm University1, Stockholm Environment Institute2, Australian National University3, University of Alaska Fairbanks4, Université catholique de Louvain5, University of East Anglia6, Wageningen University and Research Centre7, Royal Swedish Academy of Sciences8, University of Oxford9, Potsdam Institute for Climate Impact Research10, James Cook University11, Arizona State University12, Royal Institute of Technology13, University of Minnesota14, University of Vermont15, Stockholm International Water Institute16, California State University San Marcos17, Goddard Institute for Space Studies18, Commonwealth Scientific and Industrial Research Organisation19, University of Arizona20, Max Planck Society21
TL;DR: Identifying and quantifying planetary boundaries that must not be transgressed could help prevent human activities from causing unacceptable environmental change, argue Johan Rockstrom and colleagues.
Abstract: Identifying and quantifying planetary boundaries that must not be transgressed could help prevent human activities from causing unacceptable environmental change, argue Johan Rockstrom and colleagues.
8,837 citations
Authors
Showing all 118112 results
Name | H-index | Papers | Citations |
---|---|---|---|
Walter C. Willett | 334 | 2399 | 413322 |
David J. Hunter | 213 | 1836 | 207050 |
David Miller | 203 | 2573 | 204840 |
Mark I. McCarthy | 200 | 1028 | 187898 |
Dennis W. Dickson | 191 | 1243 | 148488 |
David H. Weinberg | 183 | 700 | 171424 |
Eric Boerwinkle | 183 | 1321 | 170971 |
John C. Morris | 183 | 1441 | 168413 |
Aaron R. Folsom | 181 | 1118 | 134044 |
H. S. Chen | 179 | 2401 | 178529 |
Jie Zhang | 178 | 4857 | 221720 |
Jasvinder A. Singh | 176 | 2382 | 223370 |
Feng Zhang | 172 | 1278 | 181865 |
Gang Chen | 167 | 3372 | 149819 |
Hongfang Liu | 166 | 2356 | 156290 |