31 citations · 74 across the 13 of their papers we have counts for
8 papers · 1 filter
Rethinking Importance Weighting for Transfer Learning
Nan Lu, Tianyi Zhang, Tongtong Fang +2
A key assumption in supervised learning is that training and test data follow the same probability distribution. However, this fundamental assumption is not always satisfied in pra…
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records
Tianyi Zhang, Shirui Zhang, Ziwei Chen +1
Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communication technology develops rapidly and an unp…
Learning to Stop with Surprisingly Few Samples
Daniel Russo, Assaf Zeevi, Tianyi Zhang
We consider a discounted infinite horizon optimal stopping problem. If the underlying distribution is known a priori, the solution of this problem is obtained via dynamic programmi…
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
Dongbo Xi, Bowen Song, Fuzhen Zhuang +5
With the explosive growth of e-commerce, online transaction fraud has become one of the biggest challenges for e-commerce platforms. The historical behaviors of users provide rich…
A One-step Approach to Covariate Shift Adaptation
Tianyi Zhang, Ikko Yamane, Nan Lu +1
A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution. However, such an assumption is often…
Demystifying Orthogonal Monte Carlo and Beyond
Han Lin, Haoxian Chen, Tianyi Zhang +2
Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction. Due to its simplicit…
Scalable NAS with Factorizable Architectural Parameters
Lanfei Wang, Lingxi Xie, Tianyi Zhang +2
Neural Architecture Search (NAS) is an emerging topic in machine learning and computer vision. The fundamental ideology of NAS is using an automatic mechanism to replace manual des…
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
Nan Lu, Tianyi Zhang, Gang Niu +1
The recently proposed unlabeled-unlabeled (UU) classification method allows us to train a binary classifier only from two unlabeled datasets with different class priors. Since this…