943 citations · 1.1k across the 8 of their papers we have counts for
8 papers
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…
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…
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…
Learning Bounds for Open-Set Learning
Zhen Fang, Jie Lu, Anjin Liu +2
Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. In this paper, we target a more chal…
Concept Drift Detection: Dealing with MissingValues via Fuzzy Distance Estimations
Anjin Liu, Jie Lu, Guangquan Zhang
In data streams, the data distribution of arriving observations at different time points may change - a phenomenon called concept drift. While detecting concept drift is a relative…
Concept Drift Detection via Equal Intensity k-means Space Partitioning
Anjin Liu, Jie Lu, Guangquan Zhang
Data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such distribution ch…
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation
Anjin Liu, Jie Lu, Guangquan Zhang
Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved…
Learning under Concept Drift: A Review
Jie Lu, Anjin Liu, Fan Dong +3
Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techn…