A Generalization Theory for Zero-Shot Prediction
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914014931451904 |
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| author | Mehta, Ronak Harchaoui, Zaid |
| author_facet | Mehta, Ronak Harchaoui, Zaid |
| contents | A modern paradigm for generalization in machine learning and AI consists of pre-training a task-agnostic foundation model, generally obtained using self-supervised and multimodal contrastive learning. The resulting representations can be used for prediction on a downstream task for which no labeled data is available. We present a theoretical framework to better understand this approach, called zero-shot prediction. We identify the target quantities that zero-shot prediction aims to learn, or learns in passing, and the key conditional independence relationships that enable its generalization ability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09128 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Generalization Theory for Zero-Shot Prediction Mehta, Ronak Harchaoui, Zaid Machine Learning A modern paradigm for generalization in machine learning and AI consists of pre-training a task-agnostic foundation model, generally obtained using self-supervised and multimodal contrastive learning. The resulting representations can be used for prediction on a downstream task for which no labeled data is available. We present a theoretical framework to better understand this approach, called zero-shot prediction. We identify the target quantities that zero-shot prediction aims to learn, or learns in passing, and the key conditional independence relationships that enable its generalization ability. |
| title | A Generalization Theory for Zero-Shot Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.09128 |