8 citations · 25 across the 13 of their papers we have counts for
14 papers
Semi-Supervised Regression with Heteroscedastic Pseudo-Labels
Xueqing Sun, Renzhen Wang, Quanziang Wang +3
Pseudo-labeling is a commonly used paradigm in semi-supervised learning, yet its application to semi-supervised regression (SSR) remains relatively under-explored. Unlike classific…
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data
Xu Sun, Zixuan Qin, Shun Zhang +2
In the financial risk domain, particularly in credit default prediction and fraud detection, accurate identification of high-risk class instances is paramount, as their occurrence…
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
Wenkai Yang, Xiaohan Bi, Yankai Lin +3
Driven by the rapid development of Large Language Models (LLMs), LLM-based agents have been developed to handle various real-world applications, including finance, healthcare, and…
Towards Codable Watermarking for Injecting Multi-bits Information to LLMs
Lean Wang, Wenkai Yang, Deli Chen +5
As large language models (LLMs) generate texts with increasing fluency and realism, there is a growing need to identify the source of texts to prevent the abuse of LLMs. Text water…
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter
Yi Liu, Xiaohan Bi, Lei Li +3
Federated Multilingual Neural Machine Translation (Fed-MNMT) has emerged as a promising paradigm for institutions with limited language resources. This approach allows multiple ins…
Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias
Zhiyuan Zhang, Deli Chen, Hao Zhou +3
Pre-trained Language Models (PLMs) may be poisonous with backdoors or bias injected by the suspicious attacker during the fine-tuning process. A core challenge of purifying potenti…
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features
Sishuo Chen, Wenkai Yang, Xiaohan Bi +1
Detecting out-of-distribution (OOD) inputs is crucial for the safe deployment of natural language processing (NLP) models. Though existing methods, especially those based on the st…
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning
Wenkai Yang, Deli Chen, Hao Zhou +3
Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manne…
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning
Wenkai Yang, Yankai Lin, Guangxiang Zhao +3
Federated Learning has become a widely-used framework which allows learning a global model on decentralized local datasets under the condition of protecting local data privacy. How…
Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models
Zhiyuan Zhang, Lingjuan Lyu, Xingjun Ma +2
Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks. In Natural Language Processing (NLP), DNNs are often backdoored during the fine-tuning process of a larg…
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks
Sishuo Chen, Wenkai Yang, Zhiyuan Zhang +2
Natural language processing (NLP) models are known to be vulnerable to backdoor attacks, which poses a newly arisen threat to NLP models. Prior online backdoor defense methods for…
Holistic Sentence Embeddings for Better Out-of-Distribution Detection
Sishuo Chen, Xiaohan Bi, Rundong Gao +1
Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PL…
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation
Zhiyuan Zhang, Qi Su, Xu Sun
Despite the potential of federated learning, it is known to be vulnerable to backdoor attacks. Many robust federated aggregation methods are proposed to reduce the potential backdo…
Stock Trading Volume Prediction with Dual-Process Meta-Learning
Ruibo Chen, Wei Li, Zhiyuan Zhang +3
Volume prediction is one of the fundamental objectives in the Fintech area, which is helpful for many downstream tasks, e.g., algorithmic trading. Previous methods mostly learn a u…