38 citations · 81 across the 29 of their papers we have counts for
9 papers · 1 filter
Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming
Ziwen Liu, Yan Liu, Congying Han +3
Chance-constrained programs (CCPs) optimize decisions under uncertainty by limiting the probability of constraint violation. Despite advances in traditional and learning-based appr…
Mitigating Distribution Shift in Model-based Offline RL via Shifts-aware Reward Learning
Wang Luo, Haoran Li, Zicheng Zhang +4
Model-based offline reinforcement learning trains policies using pre-collected datasets and learned environment models, eliminating the need for direct real-world environment inter…
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
Weichen Zhao, Chenguang Wang, Xinyan Wang +3
This paper presents an analytical study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). Generalizing beyond extant approaches grounded in random walk an…
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Haoran Li, Zicheng Zhang, Wang Luo +4
Establishing robust policies is essential to counter attacks or disturbances affecting deep reinforcement learning (DRL) agents. Recent studies explore state-adversarial robustness…
CARSS: Cooperative Attention-guided Reinforcement Subpath Synthesis for Solving Traveling Salesman Problem
Yuchen Shi, Congying Han, Tiande Guo
This paper introduces CARSS (Cooperative Attention-guided Reinforcement Subpath Synthesis), a novel approach to address the Traveling Salesman Problem (TSP) by leveraging cooperati…
Comprehensive Analysis of Over-smoothing in Graph Neural Networks from Markov Chains Perspective
Weichen Zhao, Chenguang Wang, Congying Han +1
The over-smoothing problem is an obstacle of developing deep graph neural network (GNN). Although many approaches to improve the over-smoothing problem have been proposed, there is…
NeuroPrim: An Attention-based Model for Solving NP-hard Spanning Tree Problems
Yuchen Shi, Congying Han, Tiande Guo
Spanning tree problems with specialized constraints can be difficult to solve in real-world scenarios, often requiring intricate algorithmic design and exponential time. Recently,…
A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
Chenguang Wang, Yaodong Yang, Oliver Slumbers +4
In this paper, we introduce a two-player zero-sum framework between a trainable \emph{Solver} and a \emph{Data Generator} to improve the generalization ability of deep learning-bas…
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization
Chenguang Wang, Ziwen Liu
In this paper, we introduce a self-supervised learning method to enhance the graph-level representations with the help of a set of subgraphs. For this purpose, we propose a univers…