Prediction-Oriented Subsampling from Data Streams
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918256531472384 |
|---|---|
| author | Mussati, Benedetta Lavinia Smith, Freddie Bickford Rainforth, Tom Roberts, Stephen |
| author_facet | Mussati, Benedetta Lavinia Smith, Freddie Bickford Rainforth, Tom Roberts, Stephen |
| contents | Data is often generated in streams, with new observations arriving over time. A key challenge for learning models from data streams is capturing relevant information while keeping computational costs manageable. We explore intelligent data subsampling for offline learning, and argue for an information-theoretic method centred on reducing uncertainty in downstream predictions of interest. Empirically, we demonstrate that this prediction-oriented approach performs better than a previously proposed information-theoretic technique on two widely studied problems. At the same time, we highlight that reliably achieving strong performance in practice requires careful model design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prediction-Oriented Subsampling from Data Streams Mussati, Benedetta Lavinia Smith, Freddie Bickford Rainforth, Tom Roberts, Stephen Machine Learning Data is often generated in streams, with new observations arriving over time. A key challenge for learning models from data streams is capturing relevant information while keeping computational costs manageable. We explore intelligent data subsampling for offline learning, and argue for an information-theoretic method centred on reducing uncertainty in downstream predictions of interest. Empirically, we demonstrate that this prediction-oriented approach performs better than a previously proposed information-theoretic technique on two widely studied problems. At the same time, we highlight that reliably achieving strong performance in practice requires careful model design. |
| title | Prediction-Oriented Subsampling from Data Streams |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.03868 |