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arXiv Machine Learning By Liuyuan Jiang, Chentong Huang, Lisha Chen

SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front

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The paper introduces SURF, a method that adjusts scalarization weights to uniformly sample the Pareto front in multi-objective optimization. By analyzing the geometry of scalarization paths, the authors derive a cumulative distribution function that maps weights to equal arc-length intervals, enabling more uniform coverage. SURF provides analytical solutions for structured problems and an iterative reconstruction approach for general cases, with theoretical guarantees and empirical evidence of improved uniformity over existing baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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