943 citations · 1.4k across the 31 of their papers we have counts for
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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…
Learning Deep Kernels for Non-Parametric Two-Sample Tests
Feng Liu, Wenkai Xu, Jie Lu +3
We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution. Our tests are constructed from kernels…