Luis Rademacher

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Navin GoyalMicrosoft ResearchVerified email at microsoft.com
Santosh S. VempalaGeorgia TechVerified email at gatech.edu
Mikhail (Misha) BelkinProfessor of Data Science, Halıcıoğlu Data Science Institute, CSE, UCSD, Amazon ScholarVerified email at ucsd.edu
James VossGoogleVerified email at google.com
Amit DeshpandeMicrosoft ResearchVerified email at microsoft.com
Joseph AndersonAssistant Professor, Salisbury UniversityVerified email at salisbury.edu
Anupama NandiThe Ohio State UniversityVerified email at buckeyemail.osu.edu
Bridget Eileen TennerProfessor of Mathematics, DePaul UniversityVerified email at math.depaul.edu
Per-Olof PerssonUniversity of California, BerkeleyVerified email at berkeley.edu
Alan FriezeCarnegie Mellon UniversityVerified email at random.math.cmu.edu
Heiko RöglinProfessor of Computer Science, University of BonnVerified email at cs.uni-bonn.de
Jesus De LoeraProfessor of Mathematics, University of California, DavisVerified email at math.ucdavis.edu
Jamie HaddockAssistant Professor, Harvey Mudd CollegeVerified email at g.hmc.edu
Juan Pablo VielmaResearch Scientist, GoogleVerified email at google.com
Alejandro TorielloProfessor of Industrial and Systems Engineering, Georgia Institute of TechnologyVerified email at isye.gatech.edu
Amirhesam AbedsoltanUniversity of California SanDiegoVerified email at ucsd.edu
Parthe PanditThakur Family Chair Assistant Professor @ IIT BombayVerified email at iitb.ac.in
Haolin ChenSalesforce AI ResearchVerified email at salesforce.com
Bruno CodenottiIIT-CNRVerified email at iit.cnr.it
Kasturi VaradarajanProfessor of Computer Science, University of IowaVerified email at uiowa.edu

Luis Rademacher

Department of Mathematics, UC Davis

Verified email at math.ucdavis.edu - Homepage

algorithms geometry randomness machine learning computational learning theory


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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	421	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	248	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	110	2014
Expanders via random spanning trees A Frieze, N Goyal, L Rademacher, S Vempala SIAM Journal on Computing 43 (2), 497-513, 2014	86*	2014
Approximating the centroid is hard LA Rademacher Proceedings of the twenty-third annual symposium on Computational geometry …, 2007	73	2007
Learning convex bodies is hard L Rademacher, N Goyal Proceedings of the 22nd Annual Conference on Learning Theory (COLT 2009 …, 2009	34*	2009
Blind signal separation in the presence of Gaussian noise M Belkin, L Rademacher, J Voss Conference on Learning Theory, 270-287, 2013	33	2013
Testing geometric convexity L Rademacher, S Vempala FSTTCS 2004: Foundations of Software Technology and Theoretical Computer …, 2005	32	2005
Fast algorithms for Gaussian noise invariant independent component analysis JR Voss, L Rademacher, M Belkin Advances in neural information processing systems 26, 2013	27	2013
Efficient learning of simplices J Anderson, N Goyal, L Rademacher Conference on Learning Theory, 1020-1045, 2013	26	2013
Dispersion of mass and the complexity of randomized geometric algorithms L Rademacher, S Vempala Advances in Mathematics 219 (3), 1037-1069, 2008	25	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	23*	2010
Eigenvectors of orthogonally decomposable functions M Belkin, L Rademacher, J Voss SIAM Journal on Computing 47 (2), 547-615, 2018	18*	2018
On the monotonicity of the expected volume of a random simplex L Rademacher Mathematika 58 (1), 77-91, 2012	18	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	16*	2014
The hidden convexity of spectral clustering M Belkin, L Rademacher, JR Voss arXiv preprint arXiv:1403.0667, 2014	13*	2014
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	12	2015
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	11	2018
A simplicial polytope that maximizes the isotropic constant must be a simplex L Rademacher Mathematika 62 (1), 307-320, 2016	10	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	8	2017

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