35 citations · 73 across the 18 of their papers we have counts for
6 papers · 1 filter
On Large Language Model Continual Unlearning
Chongyang Gao, Lixu Wang, Kaize Ding +3
While large language models have demonstrated impressive performance across various domains and tasks, their security issues have become increasingly severe. Machine unlearning has…
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems
Ruochen Jiao, Shaoyuan Xie, Justin Yue +5
Large Language Models (LLMs) have shown significant promise in real-world decision-making tasks for embodied artificial intelligence, especially when fine-tuned to leverage their i…
Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval
Lixu Wang, Xinyu Du, Qi Zhu
Cross-domain retrieval (CDR), as a crucial tool for numerous technologies, is finding increasingly broad applications. However, existing efforts face several major issues, with the…
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Lixu Wang, Yang Zhao, Jiahua Dong +5
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued. It is this rapid…
Phase-driven Domain Generalizable Learning for Nonstationary Time Series
Payal Mohapatra, Lixu Wang, Qi Zhu
Pattern recognition is a fundamental task in continuous sensing applications, but real-world scenarios often experience distribution shifts that necessitate learning generalizable…
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection
Lixu Wang, Shichao Xu, Xinyu Du +1
Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and outliers across a range of applications. Recently, deep learning techniques have bee…