943 citations · 1.5k across the 37 of their papers we have counts for
26 papers · 1 filter
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…
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…
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…
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…
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…
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…
Online Boosting Adaptive Learning under Concept Drift for Multistream Classification
En Yu, Jie Lu, Bin Zhang +1
Multistream classification poses significant challenges due to the necessity for rapid adaptation in dynamic streaming processes with concept drift. Despite the growing research ou…
Meta OOD Learning for Continuously Adaptive OOD Detection
Xinheng Wu, Jie Lu, Zhen Fang +1
Out-of-distribution (OOD) detection is crucial to modern deep learning applications by identifying and alerting about the OOD samples that should not be tested or used for making p…
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making
Tianyu Liu, Jie Lu, Zheng Yan +1
As a powerful Bayesian non-parameterized algorithm, the Gaussian process (GP) has performed a significant role in Bayesian optimization and signal processing. GPs have also advance…
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms
Guangzhi Ma, Jie Lu, Feng Liu +2
The theoretical analysis of multi-class classification has proved that the existing multi-class classification methods can train a classifier with high classification accuracy on t…
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning
Junyu Xuan, Jie Lu, Guangquan Zhang
Transfer learning where the behavior of extracting transferable knowledge from the source domain(s) and reusing this knowledge to target domain has become a research area of great…
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…
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior
Tianyu Liu, Jie Lu, Zheng Yan +1
By leveraging experience from previous tasks, meta-learning algorithms can achieve effective fast adaptation ability when encountering new tasks. However it is unclear how the gene…
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
Li Zhong, Zhen Fang, Feng Liu +3
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 learn…
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…
Learning from a Complementary-label Source Domain: Theory and Algorithms
Yiyang Zhang, Feng Liu, Zhen Fang +3
In unsupervised domain adaptation (UDA), a classifier for the target domain is trained with massive true-label data from the source domain and unlabeled data from the target domain…
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation
Yiyang Zhang, Feng Liu, Zhen Fang +3
In unsupervised domain adaptation (UDA), classifiers for the target domain are trained with massive true-label data from the source domain and unlabeled data from the target domain…
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
Li Zhong, Zhen Fang, Feng Liu +3
In the unsupervised open set domain adaptation (UOSDA), the target domain contains unknown classes that are not observed in the source domain. Researchers in this area aim to train…
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…
Open Set Domain Adaptation: Theoretical Bound and Algorithm
Zhen Fang, Jie Lu, Feng Liu +2
The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain -- t…
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
Feng Liu, Jie Lu, Bo Han +3
In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However,…