A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications

This is the official implementation for Paper

Support MOO experiments in MOP/ with problems:

Problem	Num of variables	Num of objectives	Objective function	Pareto-optimal
ex1	1	2	convex	convex
ex2	2	2	convex	convex
ex3	3	3	convex	convex
ex4	2	2	convex	convex
ZDT1	30	2	non-convex	convex
ZDT2	30	2	non-convex	non-convex
DTLZ2	10	3	non-convex	non-convex

ex3	DTLZ2

Support MTL experiments in MTL/experiments/Multi_task/ and MTL/experiments/Multi_output/ with datasets:

Dataset	Num of tasks	Website
Multi-MNIST	2
Multi-Fashion	2
Multi-Fash+MNIST	2
CelebA	40	link
NYUv2	3	link
SARCOS	7	link

Multi-MNIST	Multi-Fashion	Multi-Fash+MNIST

Requirements

• Pytorch
• Python
• Latex

Installation

pip3 install -r requirements.txt

Contact

Please create an issue or contact me through trananhtuan23012000@gmail.com, thanks!

Author

Tran Anh Tuan

Citation

If our framework is useful for your research, please consider to cite the paper:

@article{TUAN2024257,
title = {A framework for controllable Pareto front learning with completed scalarization functions and its applications},
journal = {Neural Networks},
volume = {169},
pages = {257-273},
year = {2024},
issn = {0893-6080},
doi = {https://doi.org/10.1016/j.neunet.2023.10.029},
url = {https://www.sciencedirect.com/science/article/pii/S089360802300583X},
author = {Tran Anh Tuan and Long P. Hoang and Dung D. Le and Tran Ngoc Thang},
keywords = {Multi-objective optimization, Multi-task learning, Pareto front learning, Scalarization problem, Hypernetwork},
abstract = {Pareto Front Learning (PFL) was recently introduced as an efficient method for approximating the entire Pareto front, the set of all optimal solutions to a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping between a preference vector and a Pareto optimal solution is still ambiguous, rendering its results. This study demonstrates the convergence and completion aspects of solving MOO with pseudoconvex scalarization functions and combines them into Hypernetwork in order to offer a comprehensive framework for PFL, called Controllable Pareto Front Learning. Extensive experiments demonstrate that our approach is highly accurate and significantly less computationally expensive than prior methods in term of inference time.}
}