38 citations · 80 across the 36 of their papers we have counts for
12 papers · 1 filter
On the Tension Between Optimality and Adversarial Robustness in Policy Optimization
Haoran Li, Jiayu Lv, Congying Han +5
Achieving optimality and adversarial robustness in deep reinforcement learning has long been regarded as conflicting goals. Nonetheless, recent theoretical insights presented in CA…
Purity Law for Generalizable Neural TSP Solvers
Wenzhao Liu, Haoran Li, Congying Han +3
Achieving generalization in neural approaches across different scales and distributions remains a significant challenge for the Traveling Salesman Problem~(TSP). A key obstacle is…
Towards Optimal Adversarial Robust Reinforcement Learning with Infinity Measurement Error
Haoran Li, Zicheng Zhang, Wang Luo +4
Ensuring the robustness of deep reinforcement learning (DRL) agents against adversarial attacks is critical for their trustworthy deployment. Recent research highlights the challen…
Dual Alignment Maximin Optimization for Offline Model-based RL
Chi Zhou, Wang Luo, Haoran Li +3
Offline reinforcement learning agents face significant deployment challenges due to the synthetic-to-real distribution mismatch. While most prior research has focused on improving…
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…