| Matrix approximation and projective clustering via volume sampling A Deshpande, L Rademacher, S Vempala, G Wang Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete …, 2006 | 367 | 2006 |
| Efficient volume sampling for row/column subset selection A Deshpande, L Rademacher 2010 ieee 51st annual symposium on foundations of computer science, 329-338, 2010 | 216 | 2010 |
| The more, the merrier: the blessing of dimensionality for learning large Gaussian mixtures J Anderson, M Belkin, N Goyal, L Rademacher, J Voss Conference on Learning Theory, 1135-1164, 2014 | 92 | 2014 |
| Expanders via random spanning trees A Frieze, N Goyal, L Rademacher, S Vempala SIAM Journal on Computing 43 (2), 497-513, 2014 | 78* | 2014 |
| Approximating the centroid is hard LA Rademacher Proceedings of the twenty-third annual symposium on Computational geometry …, 2007 | 62 | 2007 |
| Learning convex bodies is hard L Rademacher, N Goyal Proceedings of the 22nd Annual Conference on Learning Theory (COLT 2009 …, 2009 | 31* | 2009 |
| Testing geometric convexity L Rademacher, S Vempala FSTTCS 2004: Foundations of Software Technology and Theoretical Computer …, 2005 | 26 | 2005 |
| Blind signal separation in the presence of Gaussian noise M Belkin, L Rademacher, J Voss Conference on Learning Theory, 270-287, 2013 | 25 | 2013 |
| Efficient learning of simplices J Anderson, N Goyal, L Rademacher Conference on Learning Theory, 1020-1045, 2013 | 21 | 2013 |
| Partitioning a Planar Graph of Girth 10 into a Forest and a Matching A Bassa, J Burns, J Campbell, A Deshpande, J Farley, M Halsey, SY Ho, ... Studies in Applied Mathematics 124 (3), 213-228, 2010 | 21* | 2010 |
| Dispersion of mass and the complexity of randomized geometric algorithms L Rademacher, S Vempala Advances in Mathematics 219 (3), 1037-1069, 2008 | 21 | 2008 |
| Fast algorithms for Gaussian noise invariant independent component analysis JR Voss, L Rademacher, M Belkin Advances in neural information processing systems 26, 2013 | 20 | 2013 |
| Lower bounds for the average and smoothed number of pareto-optima T Brunsch, N Goyal, L Rademacher, H Röglin Theory of Computing 10 (1), 237-256, 2014 | 14* | 2014 |
| On the monotonicity of the expected volume of a random simplex L Rademacher Mathematika 58 (1), 77-91, 2012 | 14 | 2012 |
| Eigenvectors of orthogonally decomposable functions M Belkin, L Rademacher, J Voss SIAM Journal on Computing 47 (2), 547-615, 2018 | 13* | 2018 |
| The hidden convexity of spectral clustering M Belkin, L Rademacher, JR Voss arXiv preprint arXiv:1403.0667, 2014 | 10* | 2014 |
| The minimum Euclidean-norm point in a convex polytope: Wolfe's combinatorial algorithm is exponential JA De Loera, J Haddock, L Rademacher Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing …, 2018 | 7 | 2018 |
| A simplicial polytope that maximizes the isotropic constant must be a simplex L Rademacher Mathematika 62 (1), 307-320, 2016 | 6 | 2016 |
| A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA J Voss, M Belkin, L Rademacher Advances in Neural Information Processing Systems 28: Annual Conference on …, 2015 | 6 | 2015 |
| Heavy-tailed independent component analysis J Anderson, N Goyal, A Nandi, L Rademacher 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 290-309, 2015 | 5 | 2015 |
Navin GoyalMicrosoft ResearchVerified email at microsoft.com
