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Tammo Krueger

dblp:72/7100 · DBLP profile ↗
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5ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorSecurity and privacy · 2Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Learning theory · 67% Kernel, tree and ensemble methods · 33%
Theoretical computer science
1 paper
Information theory · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory › model selection
cross-validation
0.212015
Fast cross-validation via sequential testing · J. Mach. Learn. Res. 2015
Machine learning › Learning theory
model selection
0.212015
Fast cross-validation via sequential testing · J. Mach. Learn. Res. 2015
Information theory › statistical inference › sequential analysis
sequential detection
0.212015
Fast cross-validation via sequential testing · J. Mach. Learn. Res. 2015
Machine learning › Kernel, tree and ensemble methods
kernel methods
0.112010
Approximate Tree Kernels · J. Mach. Learn. Res. 2010
Machine learning › Kernel, tree and ensemble methods › kernel function
tree kernel
0.112010
Approximate Tree Kernels · J. Mach. Learn. Res. 2010

Methods — techniques the papers use, named apart from their topics

sequential testing · 0.4
YearPublicationVenuePosition
2018 Privacy-Enhanced Fraud Detection with Bloom Filters
Daniel Arp, Erwin Quiring, Tammo Krueger, Stanimir Dragiev, Konrad Rieck
SecureComm (1)3
2015 Fast cross-validation via sequential testing
Tammo Krueger, Danny Panknin, Mikio L. Braun
J. Mach. Learn. Res.1
2010 Cujo: efficient detection and prevention of drive-by-download attacks
abstract
The JavaScript language is a core component of active and dynamic web content in the Internet today. Besides its great success in enhancing web applications, however, JavaScript provides the basis for so-called drive-by downloads---attacks exploiting vulnerabilities in web browsers and their extensions for unnoticeably downloading malicious software. Due to the diversity and frequent use of obfuscation in these attacks, static code analysis is largely ineffective in practice. While dynamic analysis and honeypots provide means to identify drive-by-download attacks, current approaches induce a significant overhead which renders immediate prevention of attacks intractable.
Konrad Rieck, Tammo Krueger, Andreas Dewald
ACSAC2
2010 Approximate Tree Kernels
Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Robert Müller
J. Mach. Learn. Res.2
2007 Ringo - an R/Bioconductor package for analyzing ChIP-chip readouts
abstract
DOAJ is a unique and extensive index of diverse open access journals from around the world, driven by a growing community, committed to ensuring quality content is freely available online for everyone.
Joern Toedling, Oleg Sklyar, Tammo Krueger, Jenny J. Fischer, Silke Sperling, Wolfgang Huber
BMC Bioinform.3