1 citations · 1 across the 10 of their papers we have counts for
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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…