5 citations · 20 across the 17 of their papers we have counts for
10 papers · 1 filter
CoEvo: Continual Evolution of Symbolic Solutions Using Large Language Models
Ping Guo, Qingfu Zhang, Xi Lin
The discovery of symbolic solutions -- mathematical expressions, logical rules, and algorithmic structures -- is fundamental to advancing scientific and engineering progress. Howev…
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…
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume
Ping Guo, Cheng Gong, Xi Lin +2
The escalating threat of adversarial attacks on deep learning models, particularly in security-critical fields, has underscored the need for robust deep learning systems. Conventio…
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…
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
Fei Liu, Xi Lin, Zhenkun Wang +3
Vehicle routing problems (VRPs), which can be found in numerous real-world applications, have been an important research topic for several decades. Recently, the neural combinatori…
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Yifan Zhong, Chengdong Ma, Xiaoyuan Zhang +5
Current methods for large language model alignment typically use scalar human preference labels. However, this convention tends to oversimplify the multi-dimensional and heterogene…
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
Ping Guo, Fei Liu, Xi Lin +2
In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security. Decision-based attacks, which only require…
PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks
Ping Guo, Xiang Li, Zhiyuan Yang +3
Black-box query-based attacks constitute significant threats to Machine Learning as a Service (MLaaS) systems since they can generate adversarial examples without accessing the tar…