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Jianan Ye

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3 papers

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AAAI Conference 2025 Conference Paper

Disentangling Tabular Data Towards Better One-Class Anomaly Detection

Jianan Ye
Zhaorui Tan
Yijie Hu
Xi Yang
Guangliang Cheng
Kaizhu Huang

Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a single category to discern anomalies from normal data variations. Capturing the intrinsic correlation among attributes within normal samples presents one promising method for learning the concept. To do so, the most recent effort relies on a learnable mask strategy with a reconstruction task. However, this wisdom may suffer from the risk of producing uniform masks, i.e., essentially nothing is masked, leading to less effective correlation learning. To address this issue, we presume that attributes related to others in normal samples can be divided into two non-overlapping and correlated subsets, defined as CorrSets, to capture the intrinsic correlation effectively. Accordingly, we introduce an innovative method that disentangles CorrSets from normal tabular data. To our knowledge, this is a pioneering effort to apply the concept of disentanglement for one-class anomaly detection on tabular data. Extensive experiments on 20 tabular datasets show that our method substantially outperforms the state-of-the-art methods and leads to an average performance improvement of 6.1% on AUC-PR and 2.1% on AUC-ROC.

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AAAI Conference 2024 Conference Paper

MathAttack: Attacking Large Language Models towards Math Solving Ability

Zihao Zhou
Qiufeng Wang
Mingyu Jin
Jie Yao
Jianan Ye
Wei Liu
Wei Wang
Xiaowei Huang

With the boom of Large Language Models (LLMs), the research of solving Math Word Problem (MWP) has recently made great progress. However, there are few studies to examine the robustness of LLMs in math solving ability. Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of robustness in solving math problems. Compared to traditional text adversarial attack, it is essential to preserve the mathematical logic of original MWPs during the attacking. To this end, we propose logical entity recognition to identify logical entries which are then frozen. Subsequently, the remaining text are attacked by adopting a word-level attacker. Furthermore, we propose a new dataset RobustMath to evaluate the robustness of LLMs in math solving ability. Extensive experiments on our RobustMath and two another math benchmark datasets GSM8K and MultiAirth show that MathAttack could effectively attack the math solving ability of LLMs. In the experiments, we observe that (1) Our adversarial samples from higher-accuracy LLMs are also effective for attacking LLMs with lower accuracy (e.g., transfer from larger to smaller-size LLMs, or from few-shot to zero-shot prompts); (2) Complex MWPs (such as more solving steps, longer text, more numbers) are more vulnerable to attack; (3) We can improve the robustness of LLMs by using our adversarial samples in few-shot prompts. Finally, we hope our practice and observation can serve as an important attempt towards enhancing the robustness of LLMs in math solving ability. The code and dataset is available at: https://github.com/zhouzihao501/MathAttack.

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YNICL Journal 2023 Journal Article

Early detection of acute ischemic stroke using Contrast-enhanced electrical impedance tomography perfusion

Weirui Zhang
Yang Jiao
Tao Zhang
Xuechao Liu
Jianan Ye
Yuyan Zhang
Bin Yang
Meng Dai

A cerebral contrast-enhanced electrical impedance tomography perfusion method is developed for acute ischemic stroke during intravenous thrombolytic therapy. Several clinical contrast agents with stable impedance characteristics and high-conductivity contrast were screened experimentally as electrical impedance contrast agent candidates. The electrical impedance tomography perfusion method was tested on rabbits with focal cerebral infarction, and its capability for early detection was verified based on perfusion images. The experimental results showed that ioversol 350 performed significantly better as an electrical impedance contrast agent than other contrast agents (p < 0.01). Additionally, perfusion images of focal cerebral infarction in rabbits confirmed that the electrical impedance tomography perfusion method could accurately detect the location and area of different cerebral infarction lesions (p < 0.001). Therefore, the cerebral contrast-enhanced electrical impedance tomography perfusion method proposed herein combines traditional, dynamic continuous imaging with rapid detection and could be applied as an early, rapid-detection, auxiliary, bedside imaging method for patients after a suspected ischemic stroke in both prehospital and in-hospital settings.

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