Michael Langberg

Computer Networks and Communications · 2008–2015

A researcher in the Science corpus: 6 papers; trusts 20 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 30 citations into it; #6,860 of 9,886 by trust here, #5,733 by citations
• Artificial Intelligence — 14 citations into it; #12,408 of 35,345 by trust here, #25,809 by citations
• Computational Theory and Mathematics — 13 citations into it; #1,110 of 12,182 by trust here, #3,924 by citations
• Electrical and Electronic Engineering — 13 citations into it
• Computer Graphics and Computer-Aided Design — 5 citations into it
• Signal Processing — 5 citations into it

Whom Michael Langberg trusts overall

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

1. Kenneth L. Clarkson
2. Sariel Har-Peled
3. Ralf Koetter
4. Tamás Sarlós
5. Michael W. Mahoney
6. Petros Drineas
7. Aditya Ramamoorthy
8. Vladimir Naumovich Vapnik
9. David Haussler
10. T. Ho

Who trusts Michael Langberg overall

1. Dan Feldman
2. Mario Lucic
3. Ben Mussay
4. Olivier Bachem
5. Andreas Krause
6. Matthew Faulkner
7. Trevor Campbell
8. Cenk Baykal
9. Artem Barger
10. Rawad Bitar

Papers from Michael Langberg's perspective

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

1. Network information flow (IEEE Transactions on Information Theory)
2. Numerical linear algebra in the streaming model (Symposium on the Theory of Computing)
3. The benefits of coding over routing in a randomized setting (IEEE International Symposium on Information Theory. Proceedings)
4. An algebraic approach to network coding (Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252))
5. Sampling algorithms for l2 regression and applications (ACM-SIAM Symposium on Discrete Algorithms)
6. Coding for Errors and Erasures in Random Network Coding (IEEE Transactions on Information Theory)
7. CUR matrix decompositions for improved data analysis
8. Smaller Coresets for k-Median and k-Means Clustering
9. How to share a secret
10. Improved Approximation Algorithms for Large Matrices via Random Projections (IEEE Annual Symposium on Foundations of Computer Science)

Their papers

• A unified framework for approximating and clustering data (2011) — 505 citations
• Resilient Network Coding in the Presence of Byzantine Adversaries (2008) — 570 citations
• Communicating the Sum of Sources Over a Network (2010) — 83 citations
• Communication Efficient Secret Sharing (2015) — 69 citations
• Universal epsilon-approximators for Integrals (2010) — 65 citations
• Universal ε-approximators for integrals (2010) — 134 citations