Active Learning for experimental exploration

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Version vom 8. Mai 2023, 16:53 Uhr von Federico Matteucci (Diskussion | Beiträge)
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Vortragende(r) Steven Lorenz
Vortragstyp Proposal
Betreuer(in) Federico Matteucci
Termin Fr 12. Mai 2023
Vortragsmodus in Präsenz
Kurzfassung A ranking is the result of running an experiment, a set of encoders is applied to an

experimental condition (dataset, model, tuning, scoring) and are then ranked according to their performance. To draw conclusions about the performance of the encoders for a set of experimental conditions, one can aggregate the rankings into a consensus ranking. (i.e. taking the median rank) The goal of the thesis is to explore the space of consensus rankings and find all possible consensus rankings. However, running an experiment is a very time-consuming task. Therefore we utilize Active Learning, to avoid running unnecessary experiments. In Active Learning, the learner can choose the data it is trained on and achieves greater accuracy with fewer labeled data.