Ziteng Sun

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 12 papers; trusts 119 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 645 citations into it; #5,057 of 35,345 by trust here, #2,502 by citations
• Privacy & Security — 328 citations into it; #170 of 709 by trust here, #323 by citations
• Statistics and Probability — 141 citations into it; #5,814 of 12,932 by trust here, #4,964 by citations
• Safety, Robustness & Interpretability — 121 citations into it
• Computational Theory and Mathematics — 93 citations into it
• Computer Networks and Communications — 78 citations into it; #4,300 of 9,886 by trust here, #3,638 by citations
• Electrical and Electronic Engineering — 55 citations into it; #1,355 of 16,539 by trust here, #7,308 by citations
• Computer Science Applications — 53 citations into it; #591 of 1,855 by trust here, #14 by citations

Whom Ziteng Sun trusts overall

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

1. Cynthia Dwork
2. Jayadev Acharya
3. Mark Bun
4. John C. Duchi
5. Kobbi Nissim
6. Raef Bassily
7. Liam Paninski
8. Ú. Erlingsson
9. Ilias Diakonikolas
10. Frank McSherry

Who trusts Ziteng Sun overall

1. Jayadev Acharya
2. Wei-Ning Chen
3. Fan Lai
4. Clément Louis Canonne
5. Badih Ghazi
6. Peter Kairouz
7. Gautam Kamath
8. Virat Shejwalkar
9. Yuhan Liu
10. Ananda Theertha Suresh

Papers from Ziteng Sun's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• Can You Really Backdoor Federated Learning? (2019) — 742 citations
• Differentially Private Testing of Identity and Closeness of Discrete Distributions (2017) — 83 citations
• Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters (2019) — 62 citations
• Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit (2019) — 28 citations
• Communication Efficient, Sample Optimal, Linear Time Locally Private Discrete Distribution Estimation (2018) — 26 citations
• INSPECTRE: Privately Estimating the Unseen (2018) — 23 citations
• Estimating Sparse Discrete Distributions Under Local Privacy and Communication Constraints (2020) — 5 citations
• Differentially Private Assouad, Fano, and Le Cam (2020) — 76 citations
• Inference Under Information Constraints III: Local Privacy Constraints (2021) — 40 citations
• Unified lower bounds for interactive high-dimensional estimation under information constraints (2020) — 41 citations
• Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication (2018) — 173 citations