Zhiwei Steven Wu

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 20 papers; trusts 123 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 310 citations into it; #14,412 of 35,345 by trust here, #7,172 by citations
• Privacy & Security — 112 citations into it
• Safety, Robustness & Interpretability — 77 citations into it; #979 of 2,251 by trust here, #472 by citations
• Statistics and Probability — 60 citations into it
• Optimization & Training — 51 citations into it
• Computational Theory and Mathematics — 41 citations into it; #11,953 of 12,182 by trust here, #6,798 by citations
• Management Science and Operations Research — 39 citations into it
• Sociology and Political Science — 38 citations into it; #6,785 of 22,756 by trust here, #2,503 by citations

Whom Zhiwei Steven Wu trusts overall

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

1. Cynthia Dwork
2. Raef Bassily
3. Alex Krizhevsky
4. Moritz Hardt
5. Aaron Roth
6. Frank McSherry
7. Kobbi Nissim
8. Adam D. Smith
9. Kunal Talwar
10. Diederik P. Kingma

Who trusts Zhiwei Steven Wu overall

1. Dana Pessach
2. Alexandra Chouldechova
3. Aaron Roth
4. Michael Kearns
5. Simon Caton
6. Angelina Wang
7. Elliot Creager
8. Christopher Jung
9. Gautam Kamath
10. Erez Shmueli

Papers from Zhiwei Steven Wu's perspective

Ordered by how strongly the researchers Zhiwei Steven Wu 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

• An Empirical Study of Rich Subgroup Fairness for Machine Learning (2018) — 228 citations
• Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification (2020) — 122 citations
• Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness (2017) — 917 citations
• Equal Opportunity in Online Classification with Partial Feedback (2019) — 67 citations
• Fairness Incentives for Myopic Agents (2017) — 47 citations
• How to Use Heuristics for Differential Privacy (2018) — 27 citations
• Private Hypothesis Selection (2019) — 101 citations
• Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds (2020) — 31 citations
• Understanding Gradient Clipping in Private SGD: A Geometric Perspective (2020) — 250 citations
• Private Query Release Assisted by Public Data (2020) — 53 citations
• Eliciting and Enforcing Subjective Individual Fairness (2019) — 56 citations
• Fair Regression: Quantitative Definitions and Reduction-based Algorithms (2019) — 308 citations
• Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms (2019) — 89 citations
• Privacy-Preserving Distributed Deep Learning for Clinical Data (2018) — 52 citations
• Locally Private Gaussian Estimation (2018) — 39 citations
• Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing (2017) — 468 citations
• The Unfair Externalities of Exploration (2017)
• Meritocratic Fairness for Cross-Population Selection (2017) — 56 citations
• Adaptive Learning with Robust Generalization Guarantees (2016) — 63 citations
• Privacy for the Protected (Only) (2015) — 6 citations