607 citations · 1.2k across the 36 of their papers we have counts for
8 papers · 1 filter
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
Wang Lu, Jindong Wang, Xinwei Sun +4
Time series remains one of the most challenging modalities in machine learning research. The out-of-distribution (OOD) detection and generalization on time series tend to suffer du…
WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning
Xueyang Wu, Shengqi Tan, Qian Xu +1
Federated learning, as a privacy-preserving collaborative machine learning paradigm, has been gaining more and more attention in the industry. With the huge rise in demand, there h…
Easy Transfer Learning By Exploiting Intra-domain Structures
Jindong Wang, Yiqiang Chen, Han Yu +2
Transfer learning aims at transferring knowledge from a well-labeled domain to a similar but different domain with limited or no labels. Unfortunately, existing learning-based meth…
AutoML @ NeurIPS 2018 challenge: Design and Results
Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon +5
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participa…
SecureBoost: A Lossless Federated Learning Framework
Kewei Cheng, Tao Fan, Yilun Jin +4
The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (…
Parameter Transfer Unit for Deep Neural Networks
Yinghua Zhang, Yu Zhang, Qiang Yang
Parameters in deep neural networks which are trained on large-scale databases can generalize across multiple domains, which is referred as "transferability". Unfortunately, the tra…