38 citations · 81 across the 29 of their papers we have counts for
31 papers
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…
Resolving Endpoint Underfitting in Diffusion Bridges via Noise Alignment
Yurong Gao, Zicheng Zhang, Congying Han +2
Diffusion bridge models offer a powerful framework for connecting two data distributions, such as in image restoration and translation. Many existing methods learn this bridge by m…
Preference-based opponent shaping in differentiable games
Xinyu Qiao, Yudong Hu, Congying Han +2
Strategy learning in game environments with multi-agent is a challenging problem. Since each agent's reward is determined by the joint strategy, a greedy learning strategy that aim…
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…
BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video Deflickering
Xinmin Qiu, Congying Han, Zicheng Zhang +4
Developing blind video deflickering (BVD) algorithms to enhance video temporal consistency, is gaining importance amid the flourish of image processing and video generation. Howeve…
Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis
Zicheng Zhang, Ruobing Zheng, Ziwen Liu +7
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences. These i…
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…
General Method for Solving Four Types of SAT Problems
Anqi Li, Congying Han, Tiande Guo +2
Existing methods provide varying algorithms for different types of Boolean satisfiability problems (SAT), lacking a general solution framework. Accordingly, this study proposes a u…
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…
A-PSRO: A Unified Strategy Learning Method with Advantage Function for Normal-form Games
Yudong Hu, Haoran Li, Congying Han +3
Solving Nash equilibrium is the key challenge in normal-form games with large strategy spaces, where open-ended learning frameworks offer an efficient approach. In this work, we pr…
Towards Consistent Video Editing with Text-to-Image Diffusion Models
Zicheng Zhang, Bonan Li, Xuecheng Nie +3
Existing works have advanced Text-to-Image (TTI) diffusion models for video editing in a one-shot learning manner. Despite their low requirements of data and computation, these met…
DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration
Xinmin Qiu, Congying Han, Zicheng Zhang +3
Blind face restoration (BFR) is important while challenging. Prior works prefer to exploit GAN-based frameworks to tackle this task due to the balance of quality and efficiency. Ho…
Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylization
Zicheng Zhang, Yinglu Liu, Congying Han +3
Recent advances in 3D scene representation and novel view synthesis have witnessed the rise of Neural Radiance Fields (NeRFs). Nevertheless, it is not trivial to exploit NeRF for t…
StyO: Stylize Your Face in Only One-shot
Bonan Li, Zicheng Zhang, Xuecheng Nie +4
This paper focuses on face stylization with a single artistic target. Existing works for this task often fail to retain the source content while achieving geometry variation. Here,…
Applying Opponent Modeling for Automatic Bidding in Online Repeated Auctions
Yudong Hu, Congying Han, Tiande Guo +1
Online auction scenarios, such as bidding searches on advertising platforms, often require bidders to participate repeatedly in auctions for identical or similar items. Most previo…
Generating Linear programming Instances with Controllable Rank and Condition Number
Anqi Li, Congying Han, Tiande Guo
Instances generation is crucial for linear programming algorithms, which is necessary either to find the optimal pivot rules by training learning method or to evaluate and verify c…
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle
Ziwen Liu, Bonan Li, Congying Han +2
Contrastive learning (CL) has shown great power in self-supervised learning due to its ability to capture insight correlations among large-scale data. Current CL models are biased…
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…
Generalized One-shot Domain Adaptation of Generative Adversarial Networks
Zicheng Zhang, Yinglu Liu, Congying Han +3
The adaptation of a Generative Adversarial Network (GAN) aims to transfer a pre-trained GAN to a target domain with limited training data. In this paper, we focus on the one-shot c…
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,…
On the optimal pivot path of simplex method for linear programming based on reinforcement learning
Anqi Li, Tiande Guo, Congying Han +2
Based on the existing pivot rules, the simplex method for linear programming is not polynomial in the worst case. Therefore the optimal pivot of the simplex method is crucial. This…
DropKey
Bonan Li, Yinhan Hu, Xuecheng Nie +4
In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of Vision Transformer, which is important while surprisingly ignored by prior wor…
PetsGAN: Rethinking Priors for Single Image Generation
Zicheng Zhang, Yinglu Liu, Congying Han +3
Single image generation (SIG), described as generating diverse samples that have similar visual content with the given single image, is first introduced by SinGAN which builds a py…