Bolin Ding

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Microsoft

Contrastive Pre-training for Sequential Recommendation

Sequential recommendation methods play a crucial role in modern recommen...
10/27/2020 ∙ by Xu Xie, et al. ∙ 0 ∙ share

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Intertwining Order Preserving Encryption and Differential Privacy

Ciphertexts of an order-preserving encryption (OPE) scheme preserve the ...
09/11/2020 ∙ by Amrita Roy Chowdhury, et al. ∙ 0 ∙ share

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Interactive Feature Generation via Learning Adjacency Tensor of Feature Graph

To automate the generation of interactive features, recent methods are p...
07/29/2020 ∙ by Yuexiang Xie, et al. ∙ 3 ∙ share

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Simple and Deep Graph Convolutional Networks

Graph convolutional networks (GCNs) are a powerful deep learning approac...
07/04/2020 ∙ by Ming Chen, et al. ∙ 0 ∙ share

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Sequential Recommendation with Self-Attentive Multi-Adversarial Network

Recently, deep learning has made significant progress in the task of seq...
05/21/2020 ∙ by Ruiyang Ren, et al. ∙ 0 ∙ share

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Practical Data Poisoning Attack against Next-Item Recommendation

Online recommendation systems make use of a variety of information sourc...
04/07/2020 ∙ by Hengtong Zhang, et al. ∙ 0 ∙ share

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AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search

Large pre-trained language models such as BERT have shown their effectiv...
01/13/2020 ∙ by Daoyuan Chen, et al. ∙ 0 ∙ share

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Automated Relational Meta-learning

In order to efficiently learn with small amount of data on new tasks, me...
01/03/2020 ∙ by Huaxiu Yao, et al. ∙ 6 ∙ share

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Design of Algorithms under Policy-Aware Local Differential Privacy: Utility-Privacy Trade-offs

Local differential privacy (LDP) enables private data sharing and analyt...
09/25/2019 ∙ by Zhuolun Xiang, et al. ∙ 0 ∙ share

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MURS: Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...
08/30/2019 ∙ by Tianhao Wang, et al. ∙ 0 ∙ share

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Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...
08/30/2019 ∙ by Tianhao Wang, et al. ∙ 0 ∙ share

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Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment

Continuous integration is an indispensable step of modern software engin...
03/01/2019 ∙ by Cèdric Renggli, et al. ∙ 0 ∙ share

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Efficient Identification of Approximate Best Configuration of Training in Large Datasets

A configuration of training refers to the combinations of feature engine...
11/08/2018 ∙ by Silu Huang, et al. ∙ 0 ∙ share

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Towards Differentially Private Truth Discovery for Crowd Sensing Systems

Nowadays, crowd sensing becomes increasingly more popular due to the ubi...
10/10/2018 ∙ by Yaliang Li, et al. ∙ 0 ∙ share

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An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

Differential privacy has emerged as the main definition for private data...
07/02/2018 ∙ by Joshua Allen, et al. ∙ 0 ∙ share

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Comparing Population Means under Local Differential Privacy: with Significance and Power

A statistical hypothesis test determines whether a hypothesis should be ...
03/24/2018 ∙ by Bolin Ding, et al. ∙ 0 ∙ share

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Collecting Telemetry Data Privately

The collection and analysis of telemetry data from users' devices is rou...
12/05/2017 ∙ by Bolin Ding, et al. ∙ 0 ∙ share

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Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm

The rise of robotic applications has led to the generation of a huge vol...
04/16/2017 ∙ by Shaoshan Liu, et al. ∙ 0 ∙ share

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Bolin Ding

Featured Co-authors

Ce Zhang

Minlie Huang

Jie Tang

Feng Liu

Somesh Jha

Hui Wang

Min Xu

Ji-Rong Wen

Zhen Wang

Wei Lin

Fei Sun

Contrastive Pre-training for Sequential Recommendation

Intertwining Order Preserving Encryption and Differential Privacy

Interactive Feature Generation via Learning Adjacency Tensor of Feature Graph

Simple and Deep Graph Convolutional Networks

Sequential Recommendation with Self-Attentive Multi-Adversarial Network

Practical Data Poisoning Attack against Next-Item Recommendation

AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search

Automated Relational Meta-learning

Design of Algorithms under Policy-Aware Local Differential Privacy: Utility-Privacy Trade-offs

MURS: Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment

Efficient Identification of Approximate Best Configuration of Training in Large Datasets

Towards Differentially Private Truth Discovery for Crowd Sensing Systems

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

Comparing Population Means under Local Differential Privacy: with Significance and Power

Collecting Telemetry Data Privately

Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm