Luis Rademacher

Artificial Intelligence · 2006–2010

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 8 citations into it; #12,660 of 35,345 by trust here, #17,412 by citations
• Computational Theory and Mathematics — 6 citations into it; #5,888 of 12,182 by trust here, #7,851 by citations
• Computational Mechanics — 5 citations into it; #3,260 of 10,001 by trust here, #4,315 by citations

Whom Luis Rademacher trusts overall

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

1. Santosh Srinivas Vempala
2. Petros Drineas
3. Sergei A. Goreinov
4. Avner Magen
5. Sariel Har-Peled
6. Michael W. Mahoney
7. Christos Boutsidis
8. Frank McSherry
9. Dimitris Achlioptas
10. Amit Deshpande

Who trusts Luis Rademacher overall

1. Dan Feldman
2. Petros Drineas
3. David P. Woodruff
4. Peter Bhlmann
5. Charalampos E. Tsourakakis
6. Tamás Sarlós
7. Blaine Nelson
8. Christos Boutsidis
9. Maxim Sviridenko
10. Kenneth L. Clarkson

Papers from Luis Rademacher's perspective

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

1. Matrix Computations
2. Fast computation of low rank matrix approximations (Symposium on the Theory of Computing)
3. Relative-Error CUR Matrix Decompositions (SIAM Journal on Matrix Analysis and Applications)
4. An improved approximation algorithm for the column subset selection problem
5. Matrix approximation and projective clustering via volume sampling (ACM-SIAM Symposium on Discrete Algorithms)
6. Improved Approximation Algorithms for Large Matrices via Random Projections (IEEE Annual Symposium on Foundations of Computer Science)
7. CUR matrix decompositions for improved data analysis
8. Data-streams and histograms (Symposium on the Theory of Computing)
9. A decentralized algorithm for spectral analysis (Symposium on the Theory of Computing)
10. On coresets for k-means and k-median clustering

Their papers

• Efficient Volume Sampling for Row/Column Subset Selection (2010) — 215 citations
• Learning Convex Bodies is Hard (2009) — 25 citations
• Matrix approximation and projective clustering via volume sampling (2006) — 386 citations