  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.
