23 citations · 31 across the 9 of their papers we have counts for
9 papers
LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
Xiaoyuan Zhang, Liang Zhao, Yingying Yu +4
Multiobjective optimization problems (MOPs) are prevalent in machine learning, with applications in multi-task learning, learning under fairness or robustness constraints, etc. Ins…
Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization
Xi Lin, Yilu Liu, Xiaoyuan Zhang +3
Multi-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods…
PMGDA: A Preference-based Multiple Gradient Descent Algorithm
Xiaoyuan Zhang, Xi Lin, Qingfu Zhang
It is desirable in many multi-objective machine learning applications, such as multi-task learning with conflicting objectives and multi-objective reinforcement learning, to find a…
UMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition
Xiaoyuan Zhang, Xi Lin, Yichi Zhang +2
Multiobjective optimization (MOO) is prevalent in numerous applications, in which a Pareto front (PF) is constructed to display optima under various preferences. Previous methods c…
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang +3
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution. In…
Dealing with Structure Constraints in Evolutionary Pareto Set Learning
Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang +1
In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given proble…
Continuation Path Learning for Homotopy Optimization
Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang +1
Homotopy optimization is a traditional method to deal with a complicated optimization problem by solving a sequence of easy-to-hard surrogate subproblems. However, this method can…
Pareto Set Learning for Expensive Multi-Objective Optimization
Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang +1
Expensive multi-objective optimization problems can be found in many real-world applications, where their objective function evaluations involve expensive computations or physical…
Simplifying Polylogarithms with Machine Learning
Aurélien Dersy, Matthew D. Schwartz, Xiaoyuan Zhang
Polylogrithmic functions, such as the logarithm or dilogarithm, satisfy a number of algebraic identities. For the logarithm, all the identities follow from the product rule. For th…