Arpita Ghosh
Computer Networks and Communications · with Boyd · 2005–2010
A researcher in the Science corpus: 6 papers; trusts 23 researchers and is trusted by 120 researchers here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 31 citations into it; #357 of 9,886 by trust here, #390 by citations
- Artificial Intelligence — 25 citations into it; #4,250 of 35,345 by trust here, #7,531 by citations
- Computational Theory and Mathematics — 25 citations into it; #6,875 of 12,182 by trust here, #10,901 by citations
- Privacy & Security — 14 citations into it; #107 of 709 by trust here, #185 by citations
- Statistics and Probability — 13 citations into it
- Algorithms & Complexity — 11 citations into it; #131 of 516 by trust here, #63 by citations
- Statistical and Nonlinear Physics — 11 citations into it; #2,035 of 10,127 by trust here, #2,578 by citations
- Numerical Analysis — 9 citations into it
Whom Arpita Ghosh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cynthia Dwork
- Lin Xiao
- Frank McSherry
- Stephen P. Boyd
- Kobbi Nissim
- David Kempe
- Michael J. Todd
- Moritz Hardt
- Kunal Talwar
- Miroslav Fiedler
Who trusts Arpita Ghosh overall
- Quan Geng
- Angelia Nedić
- Nicolas Loizou
- Aurélien Bellet
- N. Abreu
- T-H. Hubert Chan
- Peter Kairouz
- Ananda Theertha Suresh
- Badih Ghazi
- Pramod Viswanath
Papers from Arpita Ghosh's perspective
Ordered by how strongly the researchers Arpita Ghosh trusts endorse each paper. Same corpus, a different order for every researcher.
- Revealing information while preserving privacy
- ImageNet classification with deep convolutional neural networks
- Calibrating Noise to Sensitivity in Private Data Analysis
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Differential Privacy
- Towards Sharp Inapproximability For Any 2-CSP
- Privacy, accuracy, and consistency too: a holistic solution to contingency table release
- Practical privacy: the SuLQ framework
- Learning Multiple Layers of Features from Tiny Images
- Gradient-based learning applied to document recognition
Their papers
- Universally utility-maximizing privacy mechanisms (2008) — 595 citations
- Upper bounds on algebraic connectivity via convex optimization (2006) — 55 citations
- Selling privacy at auction (2010) — 425 citations
- Minimizing Effective Resistance of a Graph (2008) — 542 citations
- Randomized gossip algorithms (2006) — 2,623 citations
- Gossip algorithms: design, analysis and applications (2005) — 625 citations