1 citations · 1 across the 10 of their papers we have counts for
12 papers
When Recommendation Denoising Meets Popularity Bias: Understanding and Mitigating Their Interaction
Guohang Zeng, Jie Lu, Guangquan Zhang
Implicit feedback is the dominant data source for recommender systems, but behavioral logs are often contaminated by false-positive interactions caused by mis-clicks, biased exposu…
Autonomous Source Knowledge Selection in Multi-Domain Adaptation
Keqiuyin Li, Jie Lu, Hua Zuo +1
Unsupervised multi-domain adaptation plays a key role in transfer learning by leveraging acquired rich source information from multiple source domains to solve target task from an…
TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG
Pengqian Lu, Jie Lu, Anjin Liu +1
Detecting hallucinations in Retrieval-Augmented Generation remains a challenge. Prior approaches attribute hallucinations to a binary conflict between internal knowledge stored in…
On the Provable Importance of Gradients for Language-Assisted Image Clustering
Bo Peng, Jie Lu, Guangquan Zhang +1
This paper investigates the recently emerged problem of Language-assisted Image Clustering (LaIC), where textual semantics are leveraged to improve the discriminability of visual r…
Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation
Yi Yin, Guangquan Zhang, Hua Zuo +1
Machine learning models require datasets for effective training, but directly sharing raw data poses significant privacy risk such as membership inference attacks (MIA). To mitigat…
Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning
En Yu, Jie Lu, Kun Wang +2
Learning from multiple data streams in real-world scenarios is fundamentally challenging due to intrinsic heterogeneity and unpredictable concept drifts. Existing methods typically…
MiraGe: Multimodal Discriminative Representation Learning for Generalizable AI-Generated Image Detection
Kuo Shi, Jie Lu, Shanshan Ye +2
Recent advances in generative models have highlighted the need for robust detectors capable of distinguishing real images from AI-generated images. While existing methods perform w…
Generalized Incremental Learning under Concept Drift across Evolving Data Streams
En Yu, Jie Lu, Guangquan Zhang
Real-world data streams exhibit inherent non-stationarity characterized by concept drift, posing significant challenges for adaptive learning systems. While existing methods addres…
Early Concept Drift Detection via Prediction Uncertainty
Pengqian Lu, Jie Lu, Anjin Liu +1
Concept drift, characterized by unpredictable changes in data distribution over time, poses significant challenges to machine learning models in streaming data scenarios. Although…
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users
Guohang Zeng, Qian Zhang, Guangquan Zhang +1
Cross-Domain Recommendation (CDR) is a promising paradigm inspired by transfer learning to solve the cold-start problem in recommender systems. Existing state-of-the-art CDR method…
A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points
Zihe Liu, Jie Lu, Guangquan Zhang +1
Deep reinforcement learning is used in various domains, but usually under the assumption that the environment has stationary conditions like transitions and state distributions. Wh…
A Neighbor-Searching Discrepancy-based Drift Detection Scheme for Learning Evolving Data
Feng Gu, Jie Lu, Zhen Fang +2
Uncertain changes in data streams present challenges for machine learning models to dynamically adapt and uphold performance in real-time. Particularly, classification boundary cha…