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

Alex Gu, Songtao Lu, Parikshit Ram, Tsui-Wei Weng

Keywords: robust optimization, bilevel optimization, multi-objective optimization

TL;DR: We study a generic min-max bilevel multi-objective optimization framework with novel theoretical analysis and applications in representation learning and hyperparameter optimization

Abstract: 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 $\widetilde{\mathcal{O}}(n^{1/2} K^{-2/5})$ 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.

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