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Title: Profiling pareto front with multi-objective stein variational gradient descent
Authors: Liu, Xingchao
Liu, Qiang
Issue Date: 6-Dec-2021
Publisher: NeurIPS 2021 Thirty-fifth Conference on Neural Information Processing Systems
Citation: Liu, Xingchao, TONG XIN, Liu, Qiang (2021-12-06). Profiling pareto front with multi-objective stein variational gradient descent. Neural Information Processing Systems. ScholarBank@NUS Repository.
Abstract: Finding diverse and representative Pareto solutions from the Pareto front is a key challenge in multi-objective optimization (MOO). In this work, we propose a novel gradient-based algorithm for profiling Pareto front by using Stein variational gradient descent (SVGD). We also provide a counterpart of our method based on Langevin dynamics. Our methods iteratively update a set of points in a parallel fashion to push them towards the Pareto front using multiple gradient descent, while encouraging the diversity between the particles by using the repulsive force mechanism in SVGD, or diffusion noise in Langevin dynamics. Compared with existing gradient-based methods that require predefined preference functions, our method can work efficiently in high dimensional problems, and can obtain more diverse solutions evenly distributed in the Pareto front. Moreover, our methods are theoretically guaranteed to converge to the Pareto front. We demonstrate the effectiveness of our method, especially the SVGD algorithm, through extensive experiments, showing its superiority over existing gradient-based algorithms.
Source Title: Neural Information Processing Systems
Appears in Collections:Staff Publications

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