Compressed and Privacy-Sensitive Sparse Regression

By Shuheng Zhou, John D. Lafferty, Larry A. Wasserman. 2009

68 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Katrina Ligett
2. Larry A. Wasserman
3. Shuheng Zhou
4. Haim Sompolinsky
5. Surya Ganguli
6. Michael U Gutmann
7. Aapo Hyvärinen
8. Jukka Corander

In Computational Mechanics

1. Surya Ganguli
2. Katrina Ligett
3. Haim Sompolinsky
4. Larry A. Wasserman
5. Shuheng Zhou
6. Madhu S. Advani

In Computer Networks and Communications

1. Katrina Ligett
2. Shuheng Zhou
3. Larry A. Wasserman
4. Haim Sompolinsky
5. Surya Ganguli

Citations in the corpus

• Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. (2012, background): “D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y B ro w n U ni ve rs ity o n 04 /1 8/ 15 .”
• Differential privacy with compression (2009, methodology): “Our goal is to preserve the privacy of every individual in the database, even if the number of records in the database is very large.”
• Compressive mechanism: utilizing sparse representation in differential privacy (2011, unknown): “[48, 47, 32] apply random projections to differential privacy.”