Related papers: Functional Bilevel Optimization for Machine Learning

Functional Bilevel Optimization for Machine Learning

URL: http://arxiv.org/abs/2403.20233v3
Date: Thu, 13 Jun 2024 13:43:42 GMT
Title: Functional Bilevel Optimization for Machine Learning
Authors: Ieva Petrulionyte, Julien Mairal, Michael Arbel,
Abstract summary: We introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space. We propose scalable and efficient algorithms for the functional bilevel optimization problem.
Score: 36.081761044296904
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space. These types of problems are most often solved by using methods developed in the parametric setting, where the inner objective is strongly convex with respect to the parameters of the prediction function. The functional point of view does not rely on this assumption and notably allows using over-parameterized neural networks as the inner prediction function. We propose scalable and efficient algorithms for the functional bilevel optimization problem and illustrate the benefits of our approach on instrumental regression and reinforcement learning tasks.

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