Learning Pareto manifolds in high dimensions: How can regularization help?
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915192608129024 |
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| author | Wegel, Tobias Kovačević, Filip Ţifrea, Alexandru Yang, Fanny |
| author_facet | Wegel, Tobias Kovačević, Filip Ţifrea, Alexandru Yang, Fanny |
| contents | Simultaneously addressing multiple objectives is becoming increasingly important in modern machine learning. At the same time, data is often high-dimensional and costly to label. For a single objective such as prediction risk, conventional regularization techniques are known to improve generalization when the data exhibits low-dimensional structure like sparsity. However, it is largely unexplored how to leverage this structure in the context of multi-objective learning (MOL) with multiple competing objectives. In this work, we discuss how the application of vanilla regularization approaches can fail, and propose a two-stage MOL framework that can successfully leverage low-dimensional structure. We demonstrate its effectiveness experimentally for multi-distribution learning and fairness-risk trade-offs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08849 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Pareto manifolds in high dimensions: How can regularization help? Wegel, Tobias Kovačević, Filip Ţifrea, Alexandru Yang, Fanny Machine Learning Simultaneously addressing multiple objectives is becoming increasingly important in modern machine learning. At the same time, data is often high-dimensional and costly to label. For a single objective such as prediction risk, conventional regularization techniques are known to improve generalization when the data exhibits low-dimensional structure like sparsity. However, it is largely unexplored how to leverage this structure in the context of multi-objective learning (MOL) with multiple competing objectives. In this work, we discuss how the application of vanilla regularization approaches can fail, and propose a two-stage MOL framework that can successfully leverage low-dimensional structure. We demonstrate its effectiveness experimentally for multi-distribution learning and fairness-risk trade-offs. |
| title | Learning Pareto manifolds in high dimensions: How can regularization help? |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.08849 |