Tianhao Wang
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 10 papers; trusts 86 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 265 citations into it; #19,259 of 35,345 by trust here, #10,667 by citations
- Privacy & Security — 164 citations into it; #299 of 709 by trust here, #380 by citations
- Computer Science Applications — 54 citations into it
- Safety, Robustness & Interpretability — 51 citations into it
- Algorithms & Complexity — 39 citations into it
- Statistics and Probability — 39 citations into it; #4,251 of 12,932 by trust here, #3,102 by citations
- Information Systems — 33 citations into it; #7,976 of 8,771 by trust here, #5,078 by citations
- Sociology and Political Science — 27 citations into it
Whom Tianhao Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cynthia Dwork
- Ú. Erlingsson
- Raef Bassily
- John C. Duchi
- Adam D. Smith
- Frank McSherry
- Kobbi Nissim
- Ninghui Li
- Michael I. Jordan
- Bolin Ding
Who trusts Tianhao Wang overall
- Jinyuan Jia
- Ninghui Li
- Neil Zhenqiang Gong
- Ning Wang
- Somesh Jha
- Ahmed El Ouadrhiri
- Teng Wang
- Graham Cormode
- Badih Ghazi
- Jun Zhao
Papers from Tianhao Wang's perspective
Ordered by how strongly the researchers Tianhao Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Calibrating Noise to Sensitivity in Private Data Analysis
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Locally Differentially Private Protocols for Frequency Estimation (2017) — 650 citations
- Privacy at Scale: Local Differential Privacy in Practice (2018) — 307 citations
- When Machine Unlearning Jeopardizes Privacy (2020) — 325 citations
- Answering Multi-Dimensional Analytical Queries under Local Differential Privacy (2019) — 109 citations
- Practical and Robust Privacy Amplification with Multi-Party Differential Privacy (2019) — 11 citations
- Consistent and Accurate Frequency Oracles under Local Differential Privacy (2019) — 21 citations
- Improving utility and security of the shuffler-based differential privacy (2019) — 40 citations
- CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy (2018) — 134 citations
- Locally Differentially Private Frequent Itemset Mining (2018) — 198 citations
- Locally Differentially Private Heavy Hitter Identification (2017) — 133 citations