Jie Lü

Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning

2018 · IEEE Transactions on Fuzzy Systems

Transfer learning is gaining considerable attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy system …

How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?

2020 · arXiv (Cornell University)

Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples from the source domain and unlabeled samples from the target domain. Classical UDA learning bounds show that target risk is upper bounded …

An Efficient Bayesian Neural Network for Multiple Data Streams

2021

Spatial and temporal data such as multiple data streams often have concept drift problems, which refers to changes of the data distributions over time. Once concept drift occurs, a stationary machine learning predictor will probably …

Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms

2022 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain, in which only unlabeled and a small number of labeled data are available. This is done by leveraging knowledge acquired from …

Domain Adaptation for Image Segmentation with Category-Guide Classifier

2023

Unsupervised domain adaptation gains remarkable progress in real visual tasks by leveraging the learned knowledge from labeled source domain to solve a similar task from unlabeled target domain by adopting pre-trained large models. Fine-tuning is …

Low Complexity Wireless Interference Identification in Antagonistic Environments Based on Multi-Task Learning

2025 · IEEE Transactions on Vehicular Technology

As the fundamental premise of anti-interference communication, wireless interference identification (WII) has garnered extensive research and yielded substantial results, especially for deep learning (DL)-enabled WII. However, existing studies are conducted typically under the closed-set assumption, …

Enhancing Graph-Based Recommendations via Fuzzy Clustering and Gated Multi-View Fusion

2025

In the era of information overload, recommender systems have become indispensable for filtering and personalising content. Recent advances in Graph Convolutional Networks (GCN) have achieved state-of-the-art performance by effectively modelling high-order user-item interactions. However, data …

Pareto fronts for privacy-utility trade-offs in multi-query systems

2026 · European Journal of Operational Research

Multi-query systems have become a common paradigm for enabling flexible access to sensitive data. However, their openness introduces privacy risks, most especially from attribute inference attacks (AIAs), where adversaries infer sensitive attributes by analyzing strategically …

Artificial intelligence in recommender systems

2020 · Complex & Intelligent Systems

Abstract Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has …