Vikrant Singhal
Artificial Intelligence · 2018–2020
A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 109 citations into it; #16,639 of 35,345 by trust here, #19,873 by citations
- Privacy & Security — 60 citations into it
- Statistics and Probability — 39 citations into it
- Computational Theory and Mathematics — 19 citations into it; #5,570 of 12,182 by trust here, #5,465 by citations
- Computer Science Applications — 19 citations into it
- Statistical Learning Theory — 16 citations into it
- Electrical and Electronic Engineering — 9 citations into it
- Safety, Robustness & Interpretability — 9 citations into it
Whom Vikrant Singhal trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cynthia Dwork
- Mark Bun
- Kobbi Nissim
- Frank McSherry
- Adam D. Smith
- Thomas Steinke
- Jonathan Ullman
- Ilias Diakonikolas
- Salil P. Vadhan
- Kunal Talwar
Who trusts Vikrant Singhal overall
Papers from Vikrant Singhal's perspective
Ordered by how strongly the researchers Vikrant Singhal trusts endorse each paper. Same corpus, a different order for every researcher.
- Calibrating Noise to Sensitivity in Private Data Analysis
- Revealing information while preserving privacy
- On the geometry of differential privacy
- Local Privacy and Statistical Minimax Rates
- Differential privacy and robust statistics
- Practical privacy: the SuLQ framework
- Bounds on the sample complexity for private learning and private data release
- ImageNet classification with deep convolutional neural networks
- Smooth sensitivity and sampling in private data analysis
- Robust Traceability from Trace Amounts
Their papers
- Privately Learning High-Dimensional Distributions (2018) — 176 citations
- Private Mean Estimation of Heavy-Tailed Distributions (2020) — 118 citations
- Differentially Private Algorithms for Learning Mixtures of Separated Gaussians (2019) — 51 citations