activity
20192022
most citedHybrid Differentially Private Federated Learning on Vertically Partitioned Data

22 citations · 67 across the 10 of their papers we have counts for

collaborators

12 papers

cs.LG2022

Contrastive Multi-view Hyperbolic Hierarchical Clustering

Fangfei Lin, Bing Bai, Kun Bai +3

Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raise…

stat.ML2020

Domain Agnostic Learning for Unbiased Authentication

Jian Liang, Yuren Cao, Shuang Li +4

Authentication is the task of confirming the matching relationship between a data instance and a given identity. Typical examples of authentication problems include face recognitio…

cs.CL2020

Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark

Guanhua Zhang, Bing Bai, Jian Liang +3

Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts. Models utilizing these superficial cl…

cs.LG202022 cited

Hybrid Differentially Private Federated Learning on Vertically Partitioned Data

Chang Wang, Jian Liang, Mingkai Huang +3

We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized l…

cs.IR202016 cited

A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations

Mingkai Huang, Hao Li, Bing Bai +3

Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to t…

stat.ML202017 cited

Adversarial Infidelity Learning for Model Interpretation

Jian Liang, Bing Bai, Yuren Cao +2

Model interpretation is essential in data mining and knowledge discovery. It can help understand the intrinsic model working mechanism and check if the model has undesired characte…