A Proximal Gradient Framework for Optimization Under Uncertainty with Applications to Sparse Data Representation
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901073114955776 |
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| author | Jordan Smith |
| author_facet | Jordan Smith |
| contents | This paper investigates the application of proximal gradient methods to optimization problems arising in scenarios with inherent uncertainty, particularly focusing on challenges posed by sparse data. We propose a modified proximal gradient algorithm incorporating a robust regularization term designed to mitigate the effects of noise and missing information. The efficacy of the proposed framework is demonstrated through a case study involving sparse behavioral data, drawing connections to recent advancements in representation learning. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19026804 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Proximal Gradient Framework for Optimization Under Uncertainty with Applications to Sparse Data Representation Jordan Smith machine learning deep learning artificial intelligence This paper investigates the application of proximal gradient methods to optimization problems arising in scenarios with inherent uncertainty, particularly focusing on challenges posed by sparse data. We propose a modified proximal gradient algorithm incorporating a robust regularization term designed to mitigate the effects of noise and missing information. The efficacy of the proposed framework is demonstrated through a case study involving sparse behavioral data, drawing connections to recent advancements in representation learning. |
| title | A Proximal Gradient Framework for Optimization Under Uncertainty with Applications to Sparse Data Representation |
| topic | machine learning deep learning artificial intelligence |
| url | https://doi.org/10.5281/zenodo.19026804 |