Statistical inference on black-box generative models in the data kernel perspective space
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916752151019520 |
|---|---|
| author | Helm, Hayden Acharyya, Aranyak Duderstadt, Brandon Park, Youngser Priebe, Carey E. |
| author_facet | Helm, Hayden Acharyya, Aranyak Duderstadt, Brandon Park, Youngser Priebe, Carey E. |
| contents | Generative models are capable of producing human-expert level content across a variety of topics and domains. As the impact of generative models grows, it is necessary to develop statistical methods to understand collections of available models. These methods are particularly important in settings where the user may not have access to information related to a model's pre-training data, weights, or other relevant model-level covariates. In this paper we extend recent results on representations of black-box generative models to model-level statistical inference tasks. We demonstrate that the model-level representations are effective for multiple inference tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01106 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Statistical inference on black-box generative models in the data kernel perspective space Helm, Hayden Acharyya, Aranyak Duderstadt, Brandon Park, Youngser Priebe, Carey E. Machine Learning Generative models are capable of producing human-expert level content across a variety of topics and domains. As the impact of generative models grows, it is necessary to develop statistical methods to understand collections of available models. These methods are particularly important in settings where the user may not have access to information related to a model's pre-training data, weights, or other relevant model-level covariates. In this paper we extend recent results on representations of black-box generative models to model-level statistical inference tasks. We demonstrate that the model-level representations are effective for multiple inference tasks. |
| title | Statistical inference on black-box generative models in the data kernel perspective space |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.01106 |