2 citations · 4 across the 8 of their papers we have counts for
8 papers
SHSP: Structure-Aware Hierarchical Solution Prediction for Mixed-Integer Linear Programming
Zherong Zhang, Guanlin Li, Chengrui Gao +5
Mixed-Integer Linear Programming (MILP) is a fundamental optimization paradigm in combinatorial optimization and has been widely applied across real-world domains. Due to its NP-ha…
Learning Early-to-Final Solution Consistency for MILP Acceleration
Guanlin Li, Chengrui Gao, Chenguang Wang +6
Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making.…
Offline Model-Based Optimization by Learning to Rank
Rong-Xi Tan, Ke Xue, Shen-Huan Lyu +5
Offline model-based optimization (MBO) aims to identify a design that maximizes a black-box function using only a fixed, pre-collected dataset of designs and their corresponding sc…
Neural Solver Selection for Combinatorial Optimization
Chengrui Gao, Haopu Shang, Ke Xue +1
Machine learning has increasingly been employed to solve NP-hard combinatorial optimization problems, resulting in the emergence of neural solvers that demonstrate remarkable perfo…
Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection
Zi-Hang Cheng, Haopu Shang, Chao Qian
Network intrusion detection is one of the most important issues in the field of cyber security, and various machine learning techniques have been applied to build intrusion detecti…
Confidence-aware Contrastive Learning for Selective Classification
Yu-Chang Wu, Shen-Huan Lyu, Haopu Shang +2
Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes s…
Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy
Chengrui Gao, Haopu Shang, Ke Xue +2
Machine learning has been adapted to help solve NP-hard combinatorial optimization problems. One prevalent way is learning to construct solutions by deep neural networks, which has…
Neural Network Pruning by Cooperative Coevolution
Haopu Shang, Jia-Liang Wu, Wenjing Hong +1
Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned…