35 citations · 58 across the 14 of their papers we have counts for
11 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…
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…
DEJA VU: Continual Model Generalization For Unseen Domains
Chenxi Liu, Lixu Wang, Lingjuan Lyu +3
In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous…
Federated Class-Incremental Learning
Jiahua Dong, Lixu Wang, Zhen Fang +4
Federated learning (FL) has attracted growing attention via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume obje…