37 citations · 79 across the 7 of their papers we have counts for
9 papers
Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation
Miao Xiong, Shen Li, Wenjie Feng +3
How do we know when the predictions made by a classifier can be trusted? This is a fundamental problem that also has immense practical applicability, especially in safety-critical…
MonLAD: Money Laundering Agents Detection in Transaction Streams
Xiaobing Sun, Wenjie Feng, Shenghua Liu +5
Given a stream of money transactions between accounts in a bank, how can we accurately detect money laundering agent accounts and suspected behaviors in real-time? Money laundering…
AdaRNN: Adaptive Learning and Forecasting of Time Series
Yuntao Du, Jindong Wang, Wenjie Feng +4
Time series has wide applications in the real world and is known to be difficult to forecast. Since its statistical properties change over time, its distribution also changes tempo…
Learning Invariant Representations across Domains and Tasks
Jindong Wang, Wenjie Feng, Chang Liu +5
Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledg…
Learning to Match Distributions for Domain Adaptation
Chaohui Yu, Jindong Wang, Chang Liu +5
When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is diff…
Transfer Learning with Dynamic Distribution Adaptation
Jindong Wang, Yiqiang Chen, Wenjie Feng +3
Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from diff…