activity
20182021
most citedMitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach

31 citations · 74 across the 13 of their papers we have counts for

collaborators

13 papers

cs.LG2021

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…

cs.LG2021

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…

cs.SE2021★ 24 cited

When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward

Jiayang Song, Deyun Lyu, Zhenya Zhang +3

Cyber-physical systems (CPS) have been broadly deployed in safety-critical domains, such as automotive systems, avionics, medical devices, etc. In recent years, Artificial Intellig…

cs.CV2021★ 6 cited

Learning Cross-Scale Visual Representations for Real-Time Image Geo-Localization

Tianyi Zhang, Matthew Johnson-Roberson

Robot localization remains a challenging task in GPS denied environments. State estimation approaches based on local sensors, e.g. cameras or IMUs, are drifting-prone for long-rang…

cs.LG2021

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…

cs.IR2020★ 3 cited

ICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses

Min Fu, Jiwei Guan, Xi Zheng +6

Efficient and appropriate online customer service is essential to large e-commerce businesses. Existing solution recommendation methods for online customer service are unable to de…

cs.LG2020★ 4 cited

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…

cs.LG2020

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…

cs.LG2020★ 1 cited

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…

eess.IV2020★ 4 cited

Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention

Tianyi Zhang, Yun Gu, Xiaolin Huang +2

With the popularity of stereo cameras in computer assisted surgery techniques, a second viewpoint would provide additional information in surgery. However, how to effectively acces…

cs.LG2019

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…

cs.LG2019★ 31 cited

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…

cs.CR2018★ 1 cited

BadLink: Combining Graph and Information-Theoretical Features for Online Fraud Group Detection

Yikun Ban, Xin Liu, Tianyi Zhang +4

Frauds severely hurt many kinds of Internet businesses. Group-based fraud detection is a popular methodology to catch fraudsters who unavoidably exhibit synchronized behaviors. We…