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of 44
pro vyhledávání: '"Tornede, Alexander"'
Autor:
Lindauer, Marius, Karl, Florian, Klier, Anne, Moosbauer, Julia, Tornede, Alexander, Mueller, Andreas, Hutter, Frank, Feurer, Matthias, Bischl, Bernd
Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows, aiding the research of new ML algorithms, and contributing to the democratization of ML by
Externí odkaz:
http://arxiv.org/abs/2406.03348
Hyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML). In practice, users are often interested in multi-objective (MO) problems, i.e., optimizing potentially conflicting objectives, like accuracy and e
Externí odkaz:
http://arxiv.org/abs/2309.03581
Autor:
Tornede, Alexander, Deng, Difan, Eimer, Theresa, Giovanelli, Joseph, Mohan, Aditya, Ruhkopf, Tim, Segel, Sarah, Theodorakopoulos, Daphne, Tornede, Tanja, Wachsmuth, Henning, Lindauer, Marius
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language Models (LLMs) have experienced a rapid series of breakthroughs very r
Externí odkaz:
http://arxiv.org/abs/2306.08107
Autor:
Tornede, Tanja, Tornede, Alexander, Fehring, Lukas, Gehring, Lukas, Graf, Helena, Hanselle, Jonas, Mohr, Felix, Wever, Marcel
PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed to reduce the involved manual effort significantly. It is intended to
Externí odkaz:
http://arxiv.org/abs/2301.06348
It is well known that different algorithms perform differently well on an instance of an algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithmic problem, which is the most suitable algorithm to solve it? As such,
Externí odkaz:
http://arxiv.org/abs/2210.17341
Autor:
Schede, Elias, Brandt, Jasmin, Tornede, Alexander, Wever, Marcel, Bengs, Viktor, Hüllermeier, Eyke, Tierney, Kevin
Publikováno v:
Journal of Artificial Intelligence Research (JAIR) 75 (2022) 425-487
Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing re
Externí odkaz:
http://arxiv.org/abs/2202.01651
Autor:
Tornede, Tanja, Tornede, Alexander, Hanselle, Jonas, Wever, Marcel, Mohr, Felix, Hüllermeier, Eyke
Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine learning pipeline - tailored to the learning task (dataset) at hand. Over
Externí odkaz:
http://arxiv.org/abs/2111.05850
In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problem
Externí odkaz:
http://arxiv.org/abs/2109.06234
The notion of bounded rationality originated from the insight that perfectly rational behavior cannot be realized by agents with limited cognitive or computational resources. Research on bounded rationality, mainly initiated by Herbert Simon, has a l
Externí odkaz:
http://arxiv.org/abs/2109.04744
The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem h
Externí odkaz:
http://arxiv.org/abs/2107.09414