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Frederico Dusberger

dblp:146/4594 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 2Theory of computation · 2Graphics, computer vision, multimedia, augmented reality and games · 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.

Theoretical computer science
1 paper
Algorithms and data structures · 100%
Artificial intelligence
1 paper
Optimization for machine learning · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Optimization for machine learning
learned optimizer
0.212015
Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning · IJCAI 2015
Algorithms and data structures
dynamic programming
0.212015
Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning · IJCAI 2015
Algorithms and data structures › dynamic programming
treewidth-based dynamic programming
0.212015
Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning · IJCAI 2015

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

machine learning · 0.4
YearPublicationVenuePosition
2020 Computing secure sets in graphs using answer set programming
abstract
Abstract The notion of secure sets is a rather new concept in the area of graph theory. Applied to social network analysis, the goal is to identify groups of entities that can repel any attack or influence from the outside. In this article, we tackle this problem by utilizing Answer Set Programming (ASP). It is known that verifying whether a set is secure in a graph is already co-NP-hard. Therefore, the problem of enumerating all secure sets is challenging for ASP and its systems. In particular, encodings for this problem seem to require disjunction and also recursive aggregates. Here, we provide such encodings and analyse their performance using the Clingo system. Furthermore, we study several problem variants, including multiple secure or insecure sets, and weighted graphs.
Michael Abseher, Bernhard Bliem, Günther Charwat, Frederico Dusberger, Stefan Woltran
J. Log. Comput.4
2015 Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning
Michael Abseher, Frederico Dusberger, Nysret Musliu, Stefan Woltran
IJCAI2
2014 The D-FLAT System for Dynamic Programming on Tree Decompositions
Michael Abseher, Bernhard Bliem, Günther Charwat, Frederico Dusberger, Markus Hecher, Stefan Woltran
JELIA4