Hai Brenner

Artificial Intelligence · 2010

A researcher in the Science corpus: 2 papers; trusts 15 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #16,574 of 35,345 by trust here, #23,077 by citations
• Privacy & Security — 18 citations into it; #244 of 709 by trust here, #318 by citations
• Computational Theory and Mathematics — 13 citations into it; #5,412 of 12,182 by trust here, #5,954 by citations
• Statistical Learning Theory — 8 citations into it; #450 of 1,038 by trust here, #396 by citations

Whom Hai Brenner trusts overall

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

1. Cynthia Dwork
2. Arpita Ghosh
3. Kobbi Nissim
4. Frank McSherry
5. Oded Goldreich
6. Avrim Blum
7. Leslie G. Valiant
8. Tim Roughgarden
9. Nina Mishra
10. Kamalika Chaudhuri

Who trusts Hai Brenner overall

1. Quan Geng
2. Pramod Viswanath
3. Peter Kairouz
4. Mark Bun
5. Kamalika Chaudhuri
6. Arpita Ghosh
7. Vitaly Feldman
8. Yu-Xiang Wang
9. Thomas Steinke
10. John C. Duchi

Papers from Hai Brenner's perspective

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

1. Practical privacy: the SuLQ framework
2. Smooth sensitivity and sampling in private data analysis
3. Differentially Private Empirical Risk Minimization
4. A learning theory approach to non-interactive database privacy
5. Privacy via pseudorandom sketches
6. Pseudorandom Bits for Polynomials
7. A firm foundation for private data analysis
8. A theory of the learnable
9. What Can We Learn Privately?
10. Revealing information while preserving privacy

Their papers

• Impossibility of Differentially Private Universally Optimal Mechanisms (2010) — 94 citations
• Bounds on the sample complexity for private learning and private data release (2010) — 215 citations