Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set

TL;DR

This paper introduces Pareto Navigation Gradient Descent, a first-order algorithm that efficiently finds Pareto models optimizing a reference criterion, applicable to large-scale multi-task learning problems.

Contribution

It presents the first practical first-order method for OPT-in-Pareto, enabling efficient optimization in complex, non-convex multi-objective settings.

Findings

Efficiently finds Pareto models with a first-order method.

Demonstrates effectiveness on multi-task learning problems.

Provides theoretical convergence guarantees.

Abstract

Many modern machine learning applications, such as multi-task learning, require finding optimal model parameters to trade-off multiple objective functions that may conflict with each other. The notion of the Pareto set allows us to focus on the set of (often infinite number of) models that cannot be strictly improved. But it does not provide an actionable procedure for picking one or a few special models to return to practical users. In this paper, we consider \emph{optimization in Pareto set (OPT-in-Pareto)}, the problem of finding Pareto models that optimize an extra reference criterion function within the Pareto set. This function can either encode a specific preference from the users, or represent a generic diversity measure for obtaining a set of diversified Pareto models that are representative of the whole Pareto set. Unfortunately, despite being a highly useful framework, efficient algorithms for OPT-in-Pareto have been largely missing, especially for large-scale, non-convex, and non-linear objectives in deep learning. A naive approach is to apply Riemannian manifold gradient descent on the Pareto set, which yields a high computational cost due to the need for eigen-calculation of Hessian matrices. We propose a first-order algorithm that approximately solves OPT-in-Pareto using only gradient information, with both high practical efficiency and theoretically guaranteed convergence property. Empirically, we demonstrate that our method works efficiently for a variety of challenging multi-task-related problems.