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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.05426 |
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| _version_ | 1866911006096097280 |
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| author | Bruna, Joan Hsu, Daniel |
| author_facet | Bruna, Joan Hsu, Daniel |
| contents | We review the literature on algorithms for estimating the index space in a multi-index model. The primary focus is on computationally efficient (polynomial-time) algorithms in Gaussian space, the assumptions under which consistency is guaranteed by these methods, and their sample complexity. In many cases, a gap is observed between the sample complexity of the best known computationally efficient methods and the information-theoretical minimum. We also review algorithms based on estimating the span of gradients using nonparametric methods, and algorithms based on fitting neural networks using gradient descent |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05426 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Survey on Algorithms for multi-index models Bruna, Joan Hsu, Daniel Machine Learning Methodology We review the literature on algorithms for estimating the index space in a multi-index model. The primary focus is on computationally efficient (polynomial-time) algorithms in Gaussian space, the assumptions under which consistency is guaranteed by these methods, and their sample complexity. In many cases, a gap is observed between the sample complexity of the best known computationally efficient methods and the information-theoretical minimum. We also review algorithms based on estimating the span of gradients using nonparametric methods, and algorithms based on fitting neural networks using gradient descent |
| title | Survey on Algorithms for multi-index models |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2504.05426 |