activity
20152025
most citedLearning under Concept Drift: A Review

943 citations · 1.5k across the 37 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CL2025

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…

cs.LG2025

Autonomous Concept Drift Threshold Determination

Pengqian Lu, Jie Lu, Anjin Liu +2

Existing drift detection methods focus on designing sensitive test statistics. They treat the detection threshold as a fixed hyperparameter, set once to balance false alarms and la…

cs.LG2025

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…

cs.CV2025

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…

cs.LG2025

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…

cs.LG2025

Learning Robust Spectral Dynamics for Temporal Domain Generalization

En Yu, Jie Lu, Xiaoyu Yang +2

Modern machine learning models struggle to maintain performance in dynamic environments where temporal distribution shifts, \emph{i.e., concept drift}, are prevalent. Temporal Doma…