activity
20182021
most citedICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses

3 citations · 4 across the 3 of their papers we have counts for

collaborators

7 papers

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.IR20203 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.LG20201 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

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

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…