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arXiv · 2503.08849

Learning Pareto manifolds in high dimensions: How can regularization help?

Tobias Wegel·Filip Kovačević·Alexandru Ţifrea·Fanny Yang
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Abstract

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.

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Tobias Wegel, Filip Kovačević, Alexandru Ţifrea, Fanny Yang. 2025-03-11. Learning Pareto manifolds in high dimensions: How can regularization help?. https://arxiv.org/abs/2503.08849

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