Pareto front learning is a technique that introduces preference vectors in a neural network to approximate the Pareto front. Previous Pareto front learning methods have demonstrated high performance in approximating simple Pareto fronts. These methods often sample preference vectors from a fixed Dirichlet distribution. However, no fixed sampling distribution can be adapted to diverse Pareto fronts. Efficiently sampling preference vectors and accurately estimating the Pareto front is a challenge. To address this challenge, we propose a data-driven preference vector sampling framework for Pareto front learning. We utilize the posterior information of the objective functions to adjust the parameters of the sampling distribution flexibly. In this manner, the proposed method can sample preference vectors from the location of the Pareto front with a high probability. Moreover, we design the distribution of the preference vector as a mixture of Dirichlet distributions to improve the performance of the model in disconnected Pareto fronts. Extensive experiments validate the superiority of the proposed method compared with state-of-the-art algorithms.

翻译：帕累托前沿学习是一种通过神经网络引入偏好向量来近似帕累托前沿的技术。现有帕累托前沿学习方法在近似简单帕累托前沿时表现出较高性能，这些方法通常从固定的Dirichlet分布中采样偏好向量。然而，固定采样分布无法适应多样化的帕累托前沿。如何高效采样偏好向量并准确估计帕累托前沿仍是一个挑战。针对该问题，我们提出了一种数据驱动的偏好向量采样框架用于帕累托前沿学习。该方法利用目标函数的后验信息灵活调整采样分布的参数，从而能够以较高概率从帕累托前沿所在位置采样偏好向量。此外，我们将偏好向量分布设计为Dirichlet混合分布，以提升模型在非连续帕累托前沿上的性能。大量实验验证了该方法相较于当前最优算法的优越性。