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of 19
pro vyhledávání: '"Böther, Maximilian"'
Autor:
Böther, Maximilian, Sebastian, Abraham, Awasthi, Pranjal, Klimovic, Ana, Ramalingam, Srikumar
Many learning problems hinge on the fundamental problem of subset selection, i.e., identifying a subset of important and representative points. For example, selecting the most significant samples in ML training cannot only reduce training costs but a
Externí odkaz:
http://arxiv.org/abs/2402.16442
Autor:
Böther, Maximilian, Robroek, Ties, Gsteiger, Viktor, Holzinger, Robin, Ma, Xianzhe, Tözün, Pınar, Klimovic, Ana
In real-world machine learning (ML) pipelines, datasets are continuously growing. Models must incorporate this new training data to improve generalization and adapt to potential distribution shifts. The cost of model retraining is proportional to how
Externí odkaz:
http://arxiv.org/abs/2312.06254
Computing a directed minimum spanning tree, called arborescence, is a fundamental algorithmic problem, although not as common as its undirected counterpart. In 1967, Edmonds discussed an elegant solution. It was refined to run in $O(\min(n^2, m\log n
Externí odkaz:
http://arxiv.org/abs/2208.02590
Autor:
Böther, Maximilian, Kißig, Otto, Taraz, Martin, Cohen, Sarel, Seidel, Karen, Friedrich, Tobias
Combinatorial optimization lies at the core of many real-world problems. Especially since the rise of graph neural networks (GNNs), the deep learning community has been developing solvers that derive solutions to NP-hard problems by learning the prob
Externí odkaz:
http://arxiv.org/abs/2201.10494
Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples
Externí odkaz:
http://arxiv.org/abs/2110.11984
Autor:
Berger, Julian, Böther, Maximilian, Doskoč, Vanja, Harder, Jonathan Gadea, Klodt, Nicolas, Kötzing, Timo, Lötzsch, Winfried, Peters, Jannik, Schiller, Leon, Seifert, Lars, Wells, Armin, Wietheger, Simon
We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the languages to be learned. This abstracts a very universal learning task which c
Externí odkaz:
http://arxiv.org/abs/2010.09460
Autor:
Berger, Julian, Böther, Maximilian, Doskoč, Vanja, Harder, Jonathan Gadea, Klodt, Nicolas, Kötzing, Timo, Lötzsch, Winfried, Peters, Jannik, Schiller, Leon, Seifert, Lars, Wells, Armin, Wietheger, Simon
In language learning in the limit, the most common type of hypothesis is to give an enumerator for a language. This so-called $W$-index allows for naming arbitrary computably enumerable languages, with the drawback that even the membership problem is
Externí odkaz:
http://arxiv.org/abs/2011.09866
Autor:
Bläsius, Thomas, Böther, Maximilian, Fischbeck, Philipp, Friedrich, Tobias, Gries, Alina, Hüffner, Falk, Kißig, Otto, Lenzner, Pascal, Molitor, Louise, Schiller, Leon, Wells, Armin, Wietheger, Simon
Traditional navigation services find the fastest route for a single driver. Though always using the fastest route seems desirable for every individual, selfish behavior can have undesirable effects such as higher energy consumption and avoidable cong
Externí odkaz:
http://arxiv.org/abs/2008.10316
Autor:
Berger, Julian, Böther, Maximilian, Doskoč, Vanja, Gadea Harder, Jonathan, Klodt, Nicolas, Kötzing, Timo, Lötzsch, Winfried, Peters, Jannik, Schiller, Leon, Seifert, Lars, Wells, Armin, Wietheger, Simon
Publikováno v:
Computability; 2024, Vol. 13 Issue 3/4, p237-261, 25p
Akademický článek
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