35 citations · 73 across the 18 of their papers we have counts for
17 papers
Split Adaptation for Pre-trained Vision Transformers
Lixu Wang, Bingqi Shang, Yi Li +4
Vision Transformers (ViTs), extensively pre-trained on large-scale datasets, have become essential to foundation models, allowing excellent performance on diverse downstream tasks…
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…
Federated Continual Novel Class Learning
Lixu Wang, Chenxi Liu, Junfeng Guo +4
In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique. However, most existing FL studies assume that the data distribution remains…
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand
Junfeng Guo, Yiming Li, Lixu Wang +4
The prosperity of deep neural networks (DNNs) is largely benefited from open-source datasets, based on which users can evaluate and improve their methods. In this paper, we revisit…
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation
Jiahua Dong, Yang Cong, Gan Sun +4
3D object recognition has successfully become an appealing research topic in the real-world. However, most existing recognition models unreasonably assume that the categories of 3D…
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…
PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learning
Junfeng Guo, Ang Li, Cong Liu
While real-world applications of reinforcement learning are becoming popular, the security and robustness of RL systems are worthy of more attention and exploration. In particular,…
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
Lixu Wang, Shichao Xu, Ruiqi Xu +2
As Artificial Intelligence as a Service gains popularity, protecting well-trained models as intellectual property is becoming increasingly important. There are two common types of…
Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator
Shichao Xu, Lixu Wang, Yixuan Wang +1
Data quantity and quality are crucial factors for data-driven learning methods. In some target problem domains, there are not many data samples available, which could significantly…
Addressing Class Imbalance in Federated Learning
Lixu Wang, Shichao Xu, Xiao Wang +1
Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution an…
Eavesdrop the Composition Proportion of Training Labels in Federated Learning
Lixu Wang, Shichao Xu, Xiao Wang +1
Federated learning (FL) has recently emerged as a new form of collaborative machine learning, where a common model can be learned while keeping all the training data on local devic…