Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Alexandre V. Evfimievski"'
Autor:
Douglas Burdick, Yannis Katsis, Nancy Xin Ru Wang, Alexandre V. Evfimievski, Marina Danilevsky
Publikováno v:
Proceedings of the VLDB Endowment. 13:3433-3436
Valuable high-precision data are often published in the form of tables in both scientific and business documents. While humans can easily identify, interpret and contextualize tables, developing general-purpose automated techniques for extraction of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52908dccd45a545112369de08e92a56f
https://doi.org/10.14778/3415478.3415563
https://doi.org/10.14778/3415478.3415563
Autor:
Prithviraj Sen, Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald, Dylan Hutchison, Niketan Pansare
Publikováno v:
Proceedings of the VLDB Endowment. 11:1755-1768
Many machine learning (ML) systems allow the specification of ML algorithms by means of linear algebra programs, and automatically generate efficient execution plans. The opportunities for fused operators---in terms of fused chains of basic operators
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d2396ec407a916ada4e1f10ea9cde93
https://doi.org/10.14778/3229863.3229865
https://doi.org/10.14778/3229863.3229865
Publikováno v:
SIGMOD Conference
Efficiently computing linear algebra expressions is central to machine learning (ML) systems. Most systems support sparse formats and operations because sparse matrices are ubiquitous and their dense representation can cause prohibitive overheads. Es
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::915bb944e48f80b936c3ef7636704249
https://doi.org/10.1145/3299869.3319854
https://doi.org/10.1145/3299869.3319854
Autor:
Prithviraj Sen, Niketan Pansare, Arvind C. Surve, Frederick Reiss, Matthias Boehm, Berthold Reinwald, Alexandre V. Evfimievski, Deron Eriksson, Michael W. Dusenberry, Faraz Makari Manshadi, Shirish Tatikonda
Publikováno v:
Proceedings of the VLDB Endowment. 9:1425-1436
The rising need for custom machine learning (ML) algorithms and the growing data sizes that require the exploitation of distributed, data-parallel frameworks such as MapReduce or Spark, pose significant productivity challenges to data scientists. Apa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5b65e3efca755c897c69fdde780dd13
https://doi.org/10.14778/3007263.3007279
https://doi.org/10.14778/3007263.3007279
Autor:
Berthold Reinwald, Alexandre V. Evfimievski, Juan Soto, Volker Markl, Sebastian Schelter, Douglas Burdick
Publikováno v:
ICDE
Meta learning techniques such as cross-validation and ensemble learning are crucial for applying machine learning to real-world use cases. These techniques first generate samples from input data, and then train and evaluate machine learning models on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0aa3f425ee7a24ded57918fcea7cdff4
https://doi.org/10.1109/icde.2015.7113367
https://doi.org/10.1109/icde.2015.7113367
Autor:
Alexandre V. Evfimievski
Publikováno v:
ACM SIGKDD Explorations Newsletter. 4:43-48
Suppose there are many clients, each having some personal information, and one server, which is interested only in aggregate, statistically significant, properties of this information. The clients can protect privacy of their data by perturbing it wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b56ab287979a374d7e792f69b61e548b
https://doi.org/10.1145/772862.772869
https://doi.org/10.1145/772862.772869
Autor:
Neal R. Lewis, Doug Burdick, Alexandre V. Evfimievski, Scott Rickards, Lucian Popa, Peter Williams, Rajasekar Krishnamurthy
Publikováno v:
DSMM
There is a significant amount of "public" unstructured content available that is centered around the financial performance and counterparty relationships of a wide range of organizations, including private and public companies, governments, and publi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::530bbff4c9e5b02524859b2dfd03ee37
https://doi.org/10.1145/2630729.2630742
https://doi.org/10.1145/2630729.2630742
Autor:
Alexandre V. Evfimievski
Publikováno v:
Theoretical Computer Science. 233:191-199
Let P and Q be two parties on either side of a communication link. Assume that P has an old version of some file and wants to get a newer version from Q (who has it). Our algorithm performs this operation using poly(log|x|,log|y|,d(x,y),log(1/ε)) co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbabe6062b18b577f1718438f969cd7c
https://doi.org/10.1016/s0304-3975(98)00019-x
https://doi.org/10.1016/s0304-3975(98)00019-x
Autor:
Prithviraj Sen, Berthold Reinwald, Douglas Burdick, Matthias Boehm, Alexandre V. Evfimievski, Shirish Tatikonda, Yuanyuan Tian
Publikováno v:
SoCC
Analytics on big data range from passenger volume prediction in transportation to customer satisfaction in automotive diagnostic systems, and from correlation analysis in social media data to log analysis in manufacturing. Expressing and running thes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23ffe3bb284289682b5a7fc13daf9681
https://doi.org/10.1145/2523616.2525965
https://doi.org/10.1145/2523616.2525965
Privacy-preserving data mining (PPDM) refers to the area of data mining that seeks to safeguard sensitive information from unsolicited or unsanctioned disclosure. Most traditional data mining techniques analyze and model the data set statistically, i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad0f44c3812a9522d1f35674ab34f46f
https://doi.org/10.4018/978-1-60566-242-8.ch056
https://doi.org/10.4018/978-1-60566-242-8.ch056
