230 citations · 376 across the 34 of their papers we have counts for
35 papers
KD-Club: An Efficient Exact Algorithm with New Coloring-based Upper Bound for the Maximum k-Defective Clique Problem
Mingming Jin, Jiongzhi Zheng, Kun He
The Maximum k-Defective Clique Problem (MDCP) aims to find a maximum k-defective clique in a given graph, where a k-defective clique is a relaxation clique missing at most k edges.…
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks
Xu Han, Anmin Liu, Chenxuan Yao +2
Deep neural networks are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to the benign input. After achieving nearly 100% attack…
Rethinking the Backward Propagation for Adversarial Transferability
Xiaosen Wang, Kangheng Tong, Kun He
Transfer-based attacks generate adversarial examples on the surrogate model, which can mislead other black-box models without access, making it promising to attack real-world appli…
PIAT: Parameter Interpolation based Adversarial Training for Image Classification
Kun He, Xin Liu, Yichen Yang +4
Adversarial training has been demonstrated to be the most effective approach to defend against adversarial attacks. However, existing adversarial training methods show apparent osc…
Geometric Batch Optimization for the Packing Equal Circles in a Circle Problem on Large Scale
Jianrong Zhou, Kun He, Jiongzhi Zheng +1
The problem of packing equal circles in a circle is a classic and famous packing problem, which is well-studied in academia and has a variety of applications in industry. This prob…
Semantic Adversarial Attacks on Face Recognition through Significant Attributes
Yasmeen M. Khedr, Yifeng Xiong, Kun He
Face recognition is known to be vulnerable to adversarial face images. Existing works craft face adversarial images by indiscriminately changing a single attribute without being aw…
Two New Upper Bounds for the Maximum k-plex Problem
Jiongzhi Zheng, Mingming Jin, Kun He
A k-plex in a graph is a vertex set where each vertex is non-adjacent to at most k vertices (including itself) in this set, and the Maximum k-plex Problem (MKP) is to find the larg…
Hybrid Learning with New Value Function for the Maximum Common Subgraph Problem
Yanli Liu, Jiming Zhao, Chu-Min Li +2
Maximum Common induced Subgraph (MCS) is an important NP-hard problem with wide real-world applications. Branch-and-Bound (BnB) is the basis of a class of efficient algorithms for…
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks
Xu Han, Anmin Liu, Yifeng Xiong +2
Deep neural networks have shown to be very vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to benign inputs. After achieving impressive attac…
Robust Textual Embedding against Word-level Adversarial Attacks
Yichen Yang, Xiaosen Wang, Kun He
We attribute the vulnerability of natural language processing models to the fact that similar inputs are converted to dissimilar representations in the embedding space, leading to…
Effective Variable Depth Local Search for the Budgeted Maximum Coverage Problem
Jianrong Zhou, Jiongzhi Zheng, Kun He
We address the Budgeted Maximum Coverage Problem (BMCP), which is a natural and more practical extension of the standard 0-1 knapsack problem and the set cover problem. Given m ele…
An Efficient Algorithm for the Partitioning Min-Max Weighted Matching Problem
Yuxuan Wang, Jinyao Xie, Jiongzhi Zheng +1
The Partitioning Min-Max Weighted Matching (PMMWM) problem is an NP-hard problem that combines the problem of partitioning a group of vertices of a bipartite graph into disjoint su…
A Strengthened Branch and Bound Algorithm for the Maximum Common (Connected) Subgraph Problem
Jianrong Zhou, Kun He, Jiongzhi Zheng +2
We propose a new and strengthened Branch-and-Bound (BnB) algorithm for the maximum common (connected) induced subgraph problem based on two new operators, Long-Short Memory (LSM) a…
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack
Zhen Yu, Xiaosen Wang, Wanxiang Che +1
Existing textual adversarial attacks usually utilize the gradient or prediction confidence to generate adversarial examples, making it hard to be deployed in real-world application…
Triangle Attack: A Query-efficient Decision-based Adversarial Attack
Xiaosen Wang, Zeliang Zhang, Kangheng Tong +4
Decision-based attack poses a severe threat to real-world applications since it regards the target model as a black box and only accesses the hard prediction label. Great efforts h…
Moser-Tardos Algorithm: Beyond Shearer's Bound
Kun He, Qian Li, Xiaoming Sun
In a seminal paper (Moser and Tardos, JACM'10), Moser and Tardos developed a simple and powerful algorithm to find solutions to combinatorial problems in the variable Lov{á}sz Loca…
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers
Delv Lin, Qi Chen, Chengyu Zhou +1
Multi-Object Tracking (MOT) has achieved aggressive progress and derived many excellent deep learning trackers. Meanwhile, most deep learning models are known to be vulnerable to a…
Regional Adversarial Training for Better Robust Generalization
Chuanbiao Song, Yanbo Fan, Yichen Yang +4
Adversarial training (AT) has been demonstrated as one of the most promising defense methods against various adversarial attacks. To our knowledge, existing AT-based methods usuall…
Detecting Textual Adversarial Examples through Randomized Substitution and Vote
Xiaosen Wang, Yifeng Xiong, Kun He
A line of work has shown that natural text processing models are vulnerable to adversarial examples. Correspondingly, various defense methods are proposed to mitigate the threat of…
Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT Solvers
Jiongzhi Zheng, Kun He, Jianrong Zhou
Local search has been demonstrated as an efficient approach for two practical generalizations of the MaxSAT problem, namely Partial MaxSAT (PMS) and Weighted PMS (WPMS). In this wo…
Multi-stage Optimization based Adversarial Training
Xiaosen Wang, Chuanbiao Song, Liwei Wang +1
In the field of adversarial robustness, there is a common practice that adopts the single-step adversarial training for quickly developing adversarially robust models. However, the…
Admix: Enhancing the Transferability of Adversarial Attacks
Xiaosen Wang, Xuanran He, Jingdong Wang +1
Deep neural networks are known to be extremely vulnerable to adversarial examples under white-box setting. Moreover, the malicious adversaries crafted on the surrogate (source) mod…
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem
Jiongzhi Zheng, Kun He, Jianrong Zhou +2
We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. And we propose a variable strategy reinforced approach, denoted as VSR-LKH, wh…
Quadratic Optimization based Clique Expansion for Overlapping Community Detection
Yanhao Yang, Pan Shi, Yuyi Wang +1
Community detection is crucial for analyzing social and biological networks, and comprehensive approaches have been proposed in the last two decades. Nevertheless, finding all over…