Luis Rademacher

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 25

Top Topics

Gaussian Mixture

Top Venues

SIAM J. Comput.

Discret. Comput. Geom.

Publications

Brett Leroux, Luis Rademacher
Expansion of random 0/1 polytopes. Random Struct. Algorithms 64 (2) (2024)
Brett Leroux, Luis Rademacher
Euclidean distance compression via deep random features. CoRR (2024)
Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin
On the Nyström Approximation for Preconditioning in Kernel Machines. AISTATS (2024)
Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit, Luis Rademacher
On the Nystrom Approximation for Preconditioning in Kernel Machines. CoRR (2023)
Brett Leroux, Luis Rademacher
Improved Bounds for the Expected Number of k-Sets. Discret. Comput. Geom. 70 (3) (2023)
Brett Leroux, Luis Rademacher
Algebraic k-Sets and Generally Neighborly Embeddings. Discret. Comput. Geom. 67 (2) (2022)
Haolin Chen, Luis Rademacher
Overcomplete Order-3 Tensor Decomposition, Blind Deconvolution, and Gaussian Mixture Models. SIAM J. Math. Data Sci. 4 (1) (2022)
Luis Rademacher, Chang Shu
The smoothed complexity of Frank-Wolfe methods via conditioning of random matrices and polytopes. CoRR (2020)
Joseph Anderson, Luis Rademacher
Efficiency of the floating body as a robust measure of dispersion. SODA (2020)
Haolin Chen, Luis Rademacher
Overcomplete order-3 tensor decomposition, blind deconvolution and Gaussian mixture models. CoRR (2020)
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) (2020)
Brett Leroux, Luis Rademacher
Algebraic k-sets and generally neighborly embeddings. CoRR (2019)
Mikhail Belkin, Luis Rademacher, James R. Voss
Eigenvectors of Orthogonally Decomposable Functions. SIAM J. Comput. 47 (2) (2018)
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)
Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-Tailed Analogues of the Covariance Matrix for ICA. CoRR (2017)
Jesús A. De Loera, Jamie Haddock, Luis Rademacher
The Minimum Euclidean-Norm Point on a Convex Polytope: Wolfe's Combinatorial Algorithm is Exponential. CoRR (2017)
Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-Tailed Analogues of the Covariance Matrix for ICA. AAAI (2017)
Mikhail Belkin, Luis Rademacher, James R. Voss
Basis Learning as an Algorithmic Primitive. COLT (2016)
Luis Rademacher, Alejandro Toriello, Juan Pablo Vielma
On packing and covering polyhedra in infinite dimensions. Oper. Res. Lett. 44 (2) (2016)
James R. Voss, Mikhail Belkin, Luis Rademacher
The Hidden Convexity of Spectral Clustering. AAAI (2016)
James R. Voss, Mikhail Belkin, Luis Rademacher
Optimal Recovery in Noisy ICA. CoRR (2015)
Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-tailed Independent Component Analysis. CoRR (2015)
Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
Heavy-Tailed Independent Component Analysis. FOCS (2015)
James R. Voss, Mikhail Belkin, Luis Rademacher
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA. NIPS (2015)
Navin Goyal, Luis Rademacher, Santosh S. Vempala
Query Complexity of Sampling and Small Geometric Partitions. Comb. Probab. Comput. 24 (5) (2015)
Navin Goyal, Luis Rademacher, Santosh S. Vempala
Query complexity of sampling and small geometric partitions. CoRR (2014)
Alan M. Frieze, Navin Goyal, Luis Rademacher, Santosh S. Vempala
Expanders via Random Spanning Trees. SIAM J. Comput. 43 (2) (2014)
Mikhail Belkin, Luis Rademacher, James R. Voss
The Hidden Convexity of Spectral Clustering. CoRR (2014)
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)
Tobias Brunsch, Navin Goyal, Luis Rademacher, Heiko Röglin
Lower Bounds for the Average and Smoothed Number of Pareto-Optima. Theory Comput. 10 (2014)
Mikhail Belkin, Luis Rademacher, James R. Voss
Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive. CoRR (2014)
James R. Voss, Luis Rademacher, Mikhail Belkin
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis. NIPS (2013)
Joseph Anderson, Navin Goyal, Luis Rademacher
Efficient Learning of Simplices. COLT (2013)
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 (2013)
Mikhail Belkin, Luis Rademacher, James R. Voss
Blind Signal Separation in the Presence of Gaussian Noise. COLT (2013)
Navin Goyal, Luis Rademacher
Efficient learning of simplices CoRR (2012)
Navin Goyal, Luis Rademacher
Lower Bounds for the Average and Smoothed Number of Pareto Optima. FSTTCS (2012)
Mikhail Belkin, Luis Rademacher, James R. Voss
Blind Signal Separation in the Presence of Gaussian Noise CoRR (2012)
Navin Goyal, Luis Rademacher
Lower Bounds for the Average and Smoothed Number of Pareto Optima CoRR (2011)
Amit Deshpande, Luis Rademacher
Efficient Volume Sampling for Row/Column Subset Selection. FOCS (2010)
Amit Deshpande, Luis Rademacher
Efficient volume sampling for row/column subset selection CoRR (2010)
Navin Goyal, Luis Rademacher
Learning convex bodies is hard CoRR (2009)
Ravi Kannan, Luis Rademacher
Optimization of a convex program with a polynomial perturbation. Oper. Res. Lett. 37 (6) (2009)
Luis Rademacher, Navin Goyal
Learning Convex Bodies is Hard. COLT (2009)
Navin Goyal, Luis Rademacher, Santosh S. Vempala
Expanders via random spanning trees. SODA (2009)
Navin Goyal, Luis Rademacher, Santosh S. Vempala
Expanders via Random Spanning Trees CoRR (2008)
Luis Rademacher
Approximating the centroid is hard. SCG (2007)
Luis Rademacher, Santosh S. Vempala
Dispersion of Mass and the Complexity of Randomized Geometric Algorithms CoRR (2006)
Bruno Codenotti, Luis Rademacher, Kasturi R. Varadarajan
Computing Equilibrium Prices in Exchange Economies with Tax Distortions. ICALP (1) (2006)
Luis Rademacher, Santosh S. Vempala
Dispersion of Mass and the Complexity of Randomized Geometric Algorithms. FOCS (2006)
Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang
Matrix approximation and projective clustering via volume sampling. SODA (2006)
Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang
Matrix Approximation and Projective Clustering via Volume Sampling. Theory Comput. 2 (12) (2006)
Luis Rademacher, Santosh S. Vempala
Dispersion of Mass and the Complexity of Randomized Geometric Algorithms. Electron. Colloquium Comput. Complex. 13 (102) (2006)
Luis Rademacher, Santosh S. Vempala
Testing Geometric Convexity. FSTTCS (2004)