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ORCIDLuis Rademacher
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2020 – today
- 2024
[j12]Brett Leroux
, Luis Rademacher:
Expansion of random 0/1 polytopes. Random Struct. Algorithms 64(2): 309-319 (2024)- [c21]Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin:
On the Nyström Approximation for Preconditioning in Kernel Machines. AISTATS 2024: 3718-3726 - [i21]Brett Leroux, Luis Rademacher:
Euclidean distance compression via deep random features. CoRR abs/2403.01327 (2024) - 2023
- [j11]Brett Leroux, Luis Rademacher:
Improved Bounds for the Expected Number of k-Sets. Discret. Comput. Geom. 70(3): 790-815 (2023) - [i20]Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit, Luis Rademacher:
On the Nystrom Approximation for Preconditioning in Kernel Machines. CoRR abs/2312.03311 (2023) - 2022
- [j10]Brett Leroux, Luis Rademacher:
Algebraic k-Sets and Generally Neighborly Embeddings. Discret. Comput. Geom. 67(2): 605-629 (2022) - [j9]Haolin Chen, Luis Rademacher:
Overcomplete Order-3 Tensor Decomposition, Blind Deconvolution, and Gaussian Mixture Models. SIAM J. Math. Data Sci. 4(1): 336-361 (2022) - 2020
- [j8]Jesús A. De Loera, Jamie Haddock, Luis Rademacher:
The Minimum Euclidean-Norm Point in a Convex Polytope: Wolfe's Combinatorial Algorithm is Exponential. SIAM J. Comput. 49(1): 138-169 (2020) - [c20]Joseph Anderson, Luis Rademacher:
Efficiency of the floating body as a robust measure of dispersion. SODA 2020: 364-377 - [i19]Haolin Chen, Luis Rademacher:
Overcomplete order-3 tensor decomposition, blind deconvolution and Gaussian mixture models. CoRR abs/2007.08133 (2020) - [i18]Luis Rademacher, Chang Shu:
The smoothed complexity of Frank-Wolfe methods via conditioning of random matrices and polytopes. CoRR abs/2009.12685 (2020)
2010 – 2019
- 2019
- [i17]Brett Leroux, Luis Rademacher:
Algebraic k-sets and generally neighborly embeddings. CoRR abs/1912.03875 (2019) - 2018
- [j7]Mikhail Belkin, Luis Rademacher, James R. Voss:
Eigenvectors of Orthogonally Decomposable Functions. SIAM J. Comput. 47(2): 547-615 (2018) - [c19]Jesús A. De Loera, Jamie Haddock, Luis Rademacher:
The minimum euclidean-norm point in a convex polytope: Wolfe's combinatorial algorithm is exponential. STOC 2018: 545-553 - 2017
- [c18]Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher:
Heavy-Tailed Analogues of the Covariance Matrix for ICA. AAAI 2017: 1712-1718 - [i16]Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher:
Heavy-Tailed Analogues of the Covariance Matrix for ICA. CoRR abs/1702.06976 (2017) - [i15]Jesús A. De Loera, Jamie Haddock, Luis Rademacher:
The Minimum Euclidean-Norm Point on a Convex Polytope: Wolfe's Combinatorial Algorithm is Exponential. CoRR abs/1710.02608 (2017) - 2016
- [j6]Luis Rademacher, Alejandro Toriello, Juan Pablo Vielma:
On packing and covering polyhedra in infinite dimensions. Oper. Res. Lett. 44(2): 225-230 (2016) - [c17]James R. Voss, Mikhail Belkin, Luis Rademacher:
The Hidden Convexity of Spectral Clustering. AAAI 2016: 2108-2114 - [c16]Mikhail Belkin, Luis Rademacher, James R. Voss:
Basis Learning as an Algorithmic Primitive. COLT 2016: 446-487 - 2015
- [j5]Navin Goyal, Luis Rademacher, Santosh S. Vempala:
Query Complexity of Sampling and Small Geometric Partitions. Comb. Probab. Comput. 24(5): 733-753 (2015) - [c15]Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher:
Heavy-Tailed Independent Component Analysis. FOCS 2015: 290-309 - [c14]James R. Voss, Mikhail Belkin, Luis Rademacher:
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA. NIPS 2015: 2872-2880 - [i14]James R. Voss, Mikhail Belkin, Luis Rademacher:
Optimal Recovery in Noisy ICA. CoRR abs/1502.04148 (2015) - [i13]Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher:
Heavy-tailed Independent Component Analysis. CoRR abs/1509.00727 (2015) - 2014
- [j4]Alan M. Frieze, Navin Goyal, Luis Rademacher, Santosh S. Vempala:
Expanders via Random Spanning Trees. SIAM J. Comput. 43(2): 497-513 (2014) - [j3]Tobias Brunsch, Navin Goyal, Luis Rademacher, Heiko Röglin:
Lower Bounds for the Average and Smoothed Number of Pareto-Optima. Theory Comput. 10: 237-256 (2014) - [c13]Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss:
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. COLT 2014: 1135-1164 - [i12]Mikhail Belkin, Luis Rademacher, James R. Voss:
The Hidden Convexity of Spectral Clustering. CoRR abs/1403.0667 (2014) - [i11]Mikhail Belkin, Luis Rademacher, James R. Voss:
Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive. CoRR abs/1411.1420 (2014) - [i10]Navin Goyal, Luis Rademacher, Santosh S. Vempala:
Query complexity of sampling and small geometric partitions. CoRR abs/1411.3799 (2014) - 2013
- [c12]Mikhail Belkin, Luis Rademacher, James R. Voss:
Blind Signal Separation in the Presence of Gaussian Noise. COLT 2013: 270-287 - [c11]Joseph Anderson, Navin Goyal, Luis Rademacher:
Efficient Learning of Simplices. COLT 2013: 1020-1045 - [c10]James R. Voss, Luis Rademacher, Mikhail Belkin:
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis. NIPS 2013: 2544-2552 - [i9]Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss:
The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. CoRR abs/1311.2891 (2013) - 2012
- [c9]Navin Goyal, Luis Rademacher:
Lower Bounds for the Average and Smoothed Number of Pareto Optima. FSTTCS 2012: 58-69 - [i8]Mikhail Belkin, Luis Rademacher, James R. Voss:
Blind Signal Separation in the Presence of Gaussian Noise. CoRR abs/1211.1716 (2012) - [i7]Navin Goyal, Luis Rademacher:
Efficient learning of simplices. CoRR abs/1211.2227 (2012) - 2011
- [i6]Navin Goyal, Luis Rademacher:
Lower Bounds for the Average and Smoothed Number of Pareto Optima. CoRR abs/1107.3876 (2011) - 2010
- [c8]Amit Deshpande, Luis Rademacher:
Efficient Volume Sampling for Row/Column Subset Selection. FOCS 2010: 329-338 - [i5]Amit Deshpande, Luis Rademacher:
Efficient volume sampling for row/column subset selection. CoRR abs/1004.4057 (2010)
2000 – 2009
- 2009
- [j2]Ravi Kannan, Luis Rademacher:
Optimization of a convex program with a polynomial perturbation. Oper. Res. Lett. 37(6): 384-386 (2009) - [c7]Luis Rademacher, Navin Goyal:
Learning Convex Bodies is Hard. COLT 2009 - [c6]Navin Goyal, Luis Rademacher, Santosh S. Vempala:
Expanders via random spanning trees. SODA 2009: 576-585 - [i4]Navin Goyal, Luis Rademacher:
Learning convex bodies is hard. CoRR abs/0904.1227 (2009) - 2008
- [i3]Navin Goyal, Luis Rademacher, Santosh S. Vempala:
Expanders via Random Spanning Trees. CoRR abs/0807.1496 (2008) - 2007
- [c5]Luis Rademacher:
Approximating the centroid is hard. SCG 2007: 302-305 - 2006
- [j1]Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang:
Matrix Approximation and Projective Clustering via Volume Sampling. Theory Comput. 2(12): 225-247 (2006) - [c4]Luis Rademacher, Santosh S. Vempala:
Dispersion of Mass and the Complexity of Randomized Geometric Algorithms. FOCS 2006: 729-738 - [c3]Bruno Codenotti, Luis Rademacher, Kasturi R. Varadarajan:
Computing Equilibrium Prices in Exchange Economies with Tax Distortions. ICALP (1) 2006: 584-595 - [c2]Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang:
Matrix approximation and projective clustering via volume sampling. SODA 2006: 1117-1126 - [i2]Luis Rademacher, Santosh S. Vempala:
Dispersion of Mass and the Complexity of Randomized Geometric Algorithms. CoRR abs/cs/0608054 (2006) - [i1]Luis Rademacher, Santosh S. Vempala:
Dispersion of Mass and the Complexity of Randomized Geometric Algorithms. Electron. Colloquium Comput. Complex. TR06 (2006) - 2004
- [c1]Luis Rademacher, Santosh S. Vempala:
Testing Geometric Convexity. FSTTCS 2004: 469-480
Coauthor Index
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