mlsanityData to 7 Oct 2026

Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning

arXiv 2203.01924 ↗First posted 3 Mar 2022cs.LG math.OCcited by 9

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Abstract (arXiv)

We consider a generic min-max multi-objective bilevel optimization problem with applications in robust machine learning such as representation learning and hyperparameter optimization. We design MORBiT, a novel single-loop gradient descent-ascent bilevel optimization algorithm, to solve the generic problem and present a novel analysis showing that MORBiT converges to the first-order stationary point at a rate of widetildeO(n1/2 K-2/5) for a class of weakly convex problems with n objectives upon K iterations of the algorithm. Our analysis utilizes novel results to handle the non-smooth min-max multi-objective setup and to obtain a sublinear dependence in the number of objectives n. Experimental results on robust representation learning and robust hyperparameter optimization showcase (i) the advantages of considering the min-max multi-objective setup, and (ii) convergence properties of the proposed MORBiT. Our code is at https://github.com/minimario/MORBiT.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 22 of its 61 references that are in the map. First posted on arXiv 3 Mar 2022.

Builds on

22 of 61 references.

References · 22

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10 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 2; 2025: 1; 2026: 2.

9 papers.