Luis Rademacher
Luis Rademacher
Department of Mathematics, UC Davis
Verified email at - Homepage
Cited by
Cited by
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
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
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
Expanders via random spanning trees
A Frieze, N Goyal, L Rademacher, S Vempala
SIAM Journal on Computing 43 (2), 497-513, 2014
Approximating the centroid is hard
LA Rademacher
Proceedings of the twenty-third annual symposium on Computational geometry …, 2007
Learning convex bodies is hard
L Rademacher, N Goyal
Proceedings of the 22nd Annual Conference on Learning Theory (COLT 2009 …, 2009
Blind signal separation in the presence of Gaussian noise
M Belkin, L Rademacher, J Voss
Conference on Learning Theory, 270-287, 2013
Testing geometric convexity
L Rademacher, S Vempala
FSTTCS 2004: Foundations of Software Technology and Theoretical Computer …, 2005
Efficient learning of simplices
J Anderson, N Goyal, L Rademacher
Conference on Learning Theory, 1020-1045, 2013
Dispersion of mass and the complexity of randomized geometric algorithms
L Rademacher, S Vempala
Advances in Mathematics 219 (3), 1037-1069, 2008
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
Fast algorithms for Gaussian noise invariant independent component analysis
JR Voss, L Rademacher, M Belkin
Advances in neural information processing systems 26, 2013
Eigenvectors of orthogonally decomposable functions
M Belkin, L Rademacher, J Voss
SIAM Journal on Computing 47 (2), 547-615, 2018
On the monotonicity of the expected volume of a random simplex
L Rademacher
Mathematika 58 (1), 77-91, 2012
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
The hidden convexity of spectral clustering
M Belkin, L Rademacher, JR Voss
arXiv preprint arXiv:1403.0667, 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
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
A simplicial polytope that maximizes the isotropic constant must be a simplex
L Rademacher
Mathematika 62 (1), 307-320, 2016
Heavy-tailed analogues of the covariance matrix for ICA
J Anderson, N Goyal, A Nandi, L Rademacher
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
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