Luis Rademacher

According to our database, Luis Rademacher authored at least 36 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Expansion of random 0/1 polytopes.

Brett Leroux, Luis Rademacher

Random Struct. Algorithms, March, 2024

Euclidean distance compression via deep random features.

Brett Leroux, Luis Rademacher

CoRR, 2024

On the Nyström Approximation for Preconditioning in Kernel Machines.

Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Improved Bounds for the Expected Number of k-Sets.

Brett Leroux, Luis Rademacher

Discret. Comput. Geom., October, 2023

2022

Overcomplete Order-3 Tensor Decomposition, Blind Deconvolution, and Gaussian Mixture Models.

Haolin Chen, Luis Rademacher

SIAM J. Math. Data Sci., 2022

Algebraic k-Sets and Generally Neighborly Embeddings.

Brett Leroux, Luis Rademacher

Discret. Comput. Geom., 2022

2020

The Minimum Euclidean-Norm Point in a Convex Polytope: Wolfe's Combinatorial Algorithm is Exponential.

Jesús A. De Loera, Jamie Haddock, Luis Rademacher

SIAM J. Comput., 2020

The smoothed complexity of Frank-Wolfe methods via conditioning of random matrices and polytopes.

Luis Rademacher, Chang Shu

CoRR, 2020

Efficiency of the floating body as a robust measure of dispersion.

Joseph Anderson, Luis Rademacher

Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

2018

Eigenvectors of Orthogonally Decomposable Functions.

Mikhail Belkin, Luis Rademacher, James R. Voss

SIAM J. Comput., 2018

2017

Heavy-Tailed Analogues of the Covariance Matrix for ICA.

Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

On packing and covering polyhedra in infinite dimensions.

Luis Rademacher, Alejandro Toriello, Juan Pablo Vielma

Oper. Res. Lett., 2016

Basis Learning as an Algorithmic Primitive.

Mikhail Belkin, Luis Rademacher, James R. Voss

Proceedings of the 29th Conference on Learning Theory, 2016

The Hidden Convexity of Spectral Clustering.

James R. Voss, Mikhail Belkin, Luis Rademacher

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Query Complexity of Sampling and Small Geometric Partitions.

Navin Goyal, Luis Rademacher, Santosh S. Vempala

Comb. Probab. Comput., 2015

Optimal Recovery in Noisy ICA.

James R. Voss, Mikhail Belkin, Luis Rademacher

CoRR, 2015

A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA.

James R. Voss, Mikhail Belkin, Luis Rademacher

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Heavy-Tailed Independent Component Analysis.

Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher

Proceedings of the IEEE 56th Annual Symposium on Foundations of Computer Science, 2015

2014

Lower Bounds for the Average and Smoothed Number of Pareto-Optima.

Tobias Brunsch, Navin Goyal, Luis Rademacher, Heiko Röglin

Theory Comput., 2014

Expanders via Random Spanning Trees.

Alan M. Frieze, Navin Goyal, Luis Rademacher, Santosh S. Vempala

SIAM J. Comput., 2014

Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive.

Mikhail Belkin, Luis Rademacher, James R. Voss

CoRR, 2014

The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures.

Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss

Proceedings of The 27th Conference on Learning Theory, 2014

2013

Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis.

James R. Voss, Luis Rademacher, Mikhail Belkin

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Blind Signal Separation in the Presence of Gaussian Noise.

Mikhail Belkin, Luis Rademacher, James R. Voss

Proceedings of the COLT 2013, 2013

Efficient Learning of Simplices.

Joseph Anderson, Navin Goyal, Luis Rademacher

Proceedings of the COLT 2013, 2013

2012

Efficient learning of simplices

Navin Goyal, Luis Rademacher

CoRR, 2012

Lower Bounds for the Average and Smoothed Number of Pareto Optima.

Navin Goyal, Luis Rademacher

Proceedings of the IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, 2012

2010

Efficient Volume Sampling for Row/Column Subset Selection.

Amit Deshpande, Luis Rademacher

Proceedings of the 51th Annual IEEE Symposium on Foundations of Computer Science, 2010

2009

Optimization of a convex program with a polynomial perturbation.

Ravi Kannan, Luis Rademacher

Oper. Res. Lett., 2009

Expanders via random spanning trees.

Navin Goyal, Luis Rademacher, Santosh S. Vempala

Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2009

Learning Convex Bodies is Hard.

Luis Rademacher, Navin Goyal

Proceedings of the COLT 2009, 2009

2007

Approximating the centroid is hard.

Luis Rademacher

Proceedings of the 23rd ACM Symposium on Computational Geometry, 2007

2006

Matrix Approximation and Projective Clustering via Volume Sampling.

Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang

Theory Comput., 2006

Dispersion of Mass and the Complexity of Randomized Geometric Algorithms.

Luis Rademacher, Santosh S. Vempala

Electron. Colloquium Comput. Complex., 2006

Computing Equilibrium Prices in Exchange Economies with Tax Distortions.

Bruno Codenotti, Luis Rademacher, Kasturi R. Varadarajan

Proceedings of the Automata, Languages and Programming, 33rd International Colloquium, 2006

2004

Testing Geometric Convexity.

Luis Rademacher, Santosh S. Vempala

Proceedings of the FSTTCS 2004: Foundations of Software Technology and Theoretical Computer Science, 2004