Xi Lin

Xi Lin (林熙)

Xi Lin is currently a Post Doctoral Research Fellow with Department of Computer Science at the City University of Hong Kong, working with Prof. Qingfu Zhang. He received the B.Sc. degree from South China University of Technology, the M.A. degree from Columbia University, and the Ph.D. degree from City University of Hong Kong under the supervision of Prof. Qingfu Zhang and Prof. Sam Kwong.

His research interests include multi-objective optimization, learning based optimization, multi-task learning and evolutionary computation. His work has been published in top-tier machine learning conferences such as ICML, NeurIPS and ICLR. He serves as an Action Editor for Transactions on Machine Learning Research (TMLR). He is a regular reviewer for many machine learning and evolutionary computation conferences and journals, and has received several outstanding reviewer awards from ICML, ICLR and TMLR.

Email / Google Scholar / Github

Selected Publications

(*: equal contribution, __: student first author I co-mentor, full list: Google Scholar)

MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework (New)
Ping Guo, Cheng Gong, Fei Liu, Xi Lin, Zhichao Lu, Qingfu Zhang, Zhenkun Wang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2025
pdf / arXiv

Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization (New)
Xi Lin, Yilu Liu, Xiaoyuan Zhang, Fei Liu, Zhenkun Wang, Qingfu Zhang
International Conference on Learning Representations (ICLR) , 2025
pdf / arXiv / openreview

Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems (New)
Fu Luo, Xi Lin, Yaoxin Wu, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang
International Conference on Learning Representations (ICLR) , 2025
pdf / openreview

Dealing with Structure Constraints in Evolutionary Pareto Set Learning
Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Qingfu Zhang
IEEE Transactions on Evolutionary Computation (TEVC) , 2025
pdf / arXiv

Multi-Objective Evolution of Heuristic Using Large Language Model
Shunyu Yao*, Fei Liu*, Xi Lin, Zhichao Lu, Zhenkun Wang, Qingfu Zhang
Annual AAAI Conference on Artificial Intelligence, (AAAI) , 2025 (Oral Presentation, Top 4.62% among All Submissions)
pdf / arXiv

Multiple Trade-offs: An Improved Approach for Lexicographic Linear Bandits
Bo Xue, Xi Lin, Xiaoyuan Zhang, Qingfu Zhang
Annual AAAI Conference on Artificial Intelligence, (AAAI) , 2025 (Oral Presentation, Top 4.62% among All Submissions)
Coming Soon

Pareto Continual Learning: Preference-Conditioned Learning and Adaption for Dynamic Stability-Plasticity Trade-off
Song Lai, Zhe Zhao, Fei Zhu, Xi Lin, Qingfu Zhang, Gaofeng Meng
Annual AAAI Conference on Artificial Intelligence, (AAAI) , 2025
Coming Soon

Gliding over the Pareto Front with Uniform Designs
Xiaoyuan Zhang, Genghui Li, Xi Lin, Yichi Zhang, Yifan Chen, Qingfu Zhang
Advances in Neural Information Processing Systems (NeurIPS) , 2024
pdf / openreview

Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Fei Liu, Zhenkun Wang, Qingfu Zhang
International Conference on Machine Learning (ICML), 2024
pdf / arXiv / openreview / code

Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, Qingfu Zhang
International Conference on Machine Learning (ICML), 2024 (Oral Presentation, Top 1.49% among All Submissions)
pdf / arXiv / openreview / code

Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
Fei Liu, Xi Lin, Zhenkun Wang, Qingfu Zhang, Xialiang Tong, Mingxuan Yuan
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024
pdf / arXiv / code

Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Fu Luo*, Xi Lin*, Fei Liu, Qingfu Zhang, Zhenkun Wang
Advances in Neural Information Processing Systems (NeurIPS) , 2023
pdf / arXiv / openreview / code

Hypervolume Maximization: A Geometric View of Pareto Set Learning
Xiaoyuan Zhang, Xi Lin, Bo Xue, Yifan Chen, Qingfu Zhang
Advances in Neural Information Processing Systems (NeurIPS) , 2023
pdf / openreview

Continuation Path Learning for Homotopy Optimization
Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang
International Conference on Machine Learning (ICML), 2023 (Oral Presentation, Top 2.37% among All Submissions)
pdf / arXiv / openreview / code

Pareto Set Learning for Expensive Multi-Objective Optimization
Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang
Advances in Neural Information Processing Systems (NeurIPS) , 2022
pdf / arXiv / openreview / code

Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
Xi Lin, Zhiyuan Yang, Qingfu Zhang
International Conference on Learning Representations (ICLR) , 2022
pdf / arXiv / openreview / code

Controllable Pareto Multi-Task Learning
Xi Lin, Zhiyuan Yang, Qingfu Zhang, Sam Kwong
Technical Report, 2020
pdf / arXiv

Pareto Multi-Task Learning
Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qingfu Zhang, Sam Kwong
Advances in Neural Information Processing Systems (NeurIPS) , 2019
pdf / arXiv / proceedings / code

Surveys

Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond
Weiyu Chen*, Xiaoyuan Zhang*, Baijiong Lin*, Xi Lin, Han Zhao, Qingfu Zhang, James T. Kwok
pdf / arXiv

A Systematic Survey on Large Language Models for Algorithm Design
Fei Liu, Yiming Yao, Ping Guo, Zhiyuan Yang, Xi Lin, Xialiang Tong, Mingxuan Yuan, Zhichao Lu, Zhenkun Wang, Qingfu Zhang
pdf / arXiv

Packages

(Please also refer to the codes above for each work.)

LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
Xiaoyuan Zhang*, Liang Zhao*, Yingying Yu*, Xi Lin, Zhenkun Wang, Han Zhao, Qingfu Zhang
Advances in Neural Information Processing Systems (NeurIPS) , 2024
pdf / arXiv / code

LLM4AD: A Platform for Algorithm Design with Large Language Model
Fei Liu*, Rui Zhang*, Zhuoliang Xie, Rui Sun, Kai Li, Xi Lin, Zhenkun Wang, Zhichao Lu, Qingfu Zhang
pdf / arXiv / code

Academic Service

Action Editor (aka Associate Editor): Transactions on Machine Learning Research (TMLR)

Conference Reviewer: ICML, ICLR, NeurIPS, AISTATS, GECCO, CEC, SSCI

Journal Reviewer: JMLR, TMLR, TEVC, TCYB, TNNLS, TAI, TETCI, NN, SWEVO, COR

Awards:
Expert Reviewer, TMLR 2023
Outstanding Reviewer, ICML 2021, ICML 2022
Highlighted Reviewer, ICLR 2022

This website's source code is adapted from Jon Barron's public academic website.