Zhanglong Ji

Artificial Intelligence · 2013–2014

A researcher in the Science corpus: 4 papers; trusts 39 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #14,359 of 35,345 by trust here, #16,771 by citations
• Privacy & Security — 36 citations into it
• Sociology and Political Science — 17 citations into it; #10,249 of 22,756 by trust here, #11,934 by citations
• Statistics and Probability — 12 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Management Science and Operations Research — 9 citations into it
• Safety, Robustness & Interpretability — 9 citations into it
• Computer Science Applications — 5 citations into it

Whom Zhanglong Ji trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Cynthia Dwork
2. Kamalika Chaudhuri
3. Frank McSherry
4. Moritz Hardt
5. Adam D. Smith
6. Avrim Blum
7. Per Austrin
8. Charles Peter Elkan
9. Jing Lei
10. Kobbi Nissim

Who trusts Zhanglong Ji overall

1. Yang Wang
2. Sean K. Simmons
3. Seyed Ali Ossia
4. Ao Liu
5. Zachary Chase Lipton
6. Quanquan Gu
7. Lingchen Zhao
8. Natalia Ponomareva
9. Charles Peter Elkan
10. Donald E. Brown

Papers from Zhanglong Ji's perspective

Ordered by how strongly the researchers Zhanglong Ji trusts endorse each paper. Same corpus, a different order for every researcher.

1. Calibrating Noise to Sensitivity in Private Data Analysis
2. Differential Privacy
3. ImageNet classification with deep convolutional neural networks
4. Differentially private recommender systems: building privacy into the net
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. Privacy, accuracy, and consistency too: a holistic solution to contingency table release
8. Differentially Private Empirical Risk Minimization
9. Learning Multiple Layers of Features from Tiny Images
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Differentially private distributed logistic regression using private and public data (2014) — 58 citations
• Differential Privacy and Machine Learning: a Survey and Review (2014) — 294 citations
• Differential privacy based on importance weighting (2013) — 43 citations
• Scalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challenge (2014) — 44 citations