Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2022
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2204.08335 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929461292695552 |
|---|---|
| author | Olmin, Amanda Lindqvist, Jakob Svensson, Lennart Lindsten, Fredrik |
| author_facet | Olmin, Amanda Lindqvist, Jakob Svensson, Lennart Lindsten, Fredrik |
| contents | Annotating data for supervised learning can be costly. When the annotation budget is limited, active learning can be used to select and annotate those observations that are likely to give the most gain in model performance. We propose an active learning algorithm that, in addition to selecting which observation to annotate, selects the precision of the annotation that is acquired. Assuming that annotations with low precision are cheaper to obtain, this allows the model to explore a larger part of the input space, with the same annotation budget. We build our acquisition function on the previously proposed BALD objective for Gaussian Processes, and empirically demonstrate the gains of being able to adjust the annotation precision in the active learning loop. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_08335 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Active Learning with Weak Supervision for Gaussian Processes Olmin, Amanda Lindqvist, Jakob Svensson, Lennart Lindsten, Fredrik Machine Learning Annotating data for supervised learning can be costly. When the annotation budget is limited, active learning can be used to select and annotate those observations that are likely to give the most gain in model performance. We propose an active learning algorithm that, in addition to selecting which observation to annotate, selects the precision of the annotation that is acquired. Assuming that annotations with low precision are cheaper to obtain, this allows the model to explore a larger part of the input space, with the same annotation budget. We build our acquisition function on the previously proposed BALD objective for Gaussian Processes, and empirically demonstrate the gains of being able to adjust the annotation precision in the active learning loop. |
| title | Active Learning with Weak Supervision for Gaussian Processes |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2204.08335 |