J. M. Chang

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it
• Safety, Robustness & Interpretability — 7 citations into it

Whom J. M. Chang trusts overall

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

1. Kamalika Chaudhuri
2. Cynthia Dwork
3. Moritz Hardt
4. Anand D. Sarwate
5. O. Ohrimenko
6. Kunal Talwar
7. Eric Price

Papers from J. M. Chang's perspective

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

1. Differentially Private Empirical Risk Minimization
2. Randomized response: a survey technique for eliminating evasive answer bias.
3. The Algorithmic Foundations of Differential Privacy
4. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
5. Near-Optimal Algorithms for Differentially-Private Principal Components
6. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet classification with deep convolutional neural networks
9. Calibrating Noise to Sensitivity in Private Data Analysis
10. Gradient-based learning applied to document recognition

Their papers

• Privacy-Preserving Machine Learning: Threats and Solutions (2018) — 415 citations