Aleksandra B. Slavkovic
Artificial Intelligence · 2009–2018
A researcher in the Science corpus: 5 papers; trusts 37 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 45 citations into it; #5,110 of 35,345 by trust here, #10,319 by citations
- Privacy & Security — 29 citations into it
- Statistics and Probability — 25 citations into it
- Computer Science Applications — 8 citations into it
- Computational Theory and Mathematics — 7 citations into it
- Safety, Robustness & Interpretability — 6 citations into it
Whom Aleksandra B. Slavkovic trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cynthia Dwork
- Adam D. Smith
- Kamalika Chaudhuri
- Kobbi Nissim
- Frank McSherry
- Quan Geng
- Mee Young Park
- Duy Vu
- Caroline Uhler
- Jing Lei
Who trusts Aleksandra B. Slavkovic overall
- Daniel Kifer
- Yue Wang
- Jordan Awan
- Jing Lei
- Marco Gaboardi
- Kazuya Kakizaki
- Simon Couch
- Aaron Johnson
- Ryan M. Rogers
- Garrett Bernstein
Papers from Aleksandra B. Slavkovic's perspective
Ordered by how strongly the researchers Aleksandra B. Slavkovic trusts endorse each paper. Same corpus, a different order for every researcher.
- Calibrating Noise to Sensitivity in Private Data Analysis
- Differential Privacy
- Smooth sensitivity and sampling in private data analysis
- Differentially Private Empirical Risk Minimization
- Privacy, accuracy, and consistency too: a holistic solution to contingency table release
- Regression Shrinkage and Selection via the Lasso
- Towards Sharp Inapproximability For Any 2-CSP
- A Statistical Framework for Differential Privacy
- Differential privacy and robust statistics
- Pseudorandom Bits for Polynomials
Their papers
- Differential Privacy for Clinical Trial Data: Preliminary Evaluations (2009) — 135 citations
- Privacy-Preserving Data Sharing for Genome-Wide Association Studies (2012) — 159 citations
- Privacy Preserving GWAS Data Sharing (2011) — 103 citations
- Differentially Private Uniformly Most Powerful Tests for Binomial Data (2018) — 58 citations
- Differentially private model selection with penalized and constrained likelihood (2016) — 34 citations