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pro vyhledávání: '"Heyden, Marco"'
Autor:
Matteucci, Federico, Arzamasov, Vadim, Cribeiro-Ramallo, Jose, Heyden, Marco, Ntounas, Konstantin, Böhm, Klemens
Experimental studies are a cornerstone of machine learning (ML) research. A common, but often implicit, assumption is that the results of a study will generalize beyond the study itself, e.g. to new data. That is, there is a high probability that rep
Externí odkaz:
http://arxiv.org/abs/2406.17374
Autor:
Heyden, Marco, Fouché, Edouard, Arzamasov, Vadim, Fenn, Tanja, Kalinke, Florian, Böhm, Klemens
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detec
Externí odkaz:
http://arxiv.org/abs/2306.12974
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from $K$ arms with unknown expected rewards and costs. The goal is to maximize the total reward under a budget constraint. A player thus seeks to choose the arm
Externí odkaz:
http://arxiv.org/abs/2306.07071
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e.g., in predictive maintenance, fraud detection, or medicine. A principled approach to detect changes is to compare the distributions of observatio
Externí odkaz:
http://arxiv.org/abs/2205.12706
Autor:
Heyden, Marco, Fouché, Edouard, Arzamasov, Vadim, Fenn, Tanja, Kalinke, Florian, Böhm, Klemens
Publikováno v:
Data Mining & Knowledge Discovery; May2024, Vol. 38 Issue 3, p1334-1363, 30p
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783031059322
PAKDD 2022-Pacific-Asia Conference on Knowledge Discovery and Data Mining
PAKDD 2022-Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2022, Chengdu, China. pp.472-484, ⟨10.1007/978-3-031-05933-9_37⟩
PAKDD 2022-Pacific-Asia Conference on Knowledge Discovery and Data Mining
PAKDD 2022-Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2022, Chengdu, China. pp.472-484, ⟨10.1007/978-3-031-05933-9_37⟩
International audience; Automated Machine Learning (AutoML) deals with finding well-performing machine learning models and their corresponding configurations without the need of machine learning experts. However, if one assumes an online learning sce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3e4de252282cc14c8030436d81b72fe
https://doi.org/10.1007/978-3-031-05933-9_37
https://doi.org/10.1007/978-3-031-05933-9_37