Luis Rademacher

56 papers A* 15A 1Journal 34Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Estranged facets and J. Appl. Probab. Brett Leroux, Luis Rademacher
2024	A*	`conf`	Euclidean distance compression via deep random features. NeurIPS Brett Leroux, Luis Rademacher
2024	J	`jnl`	Euclidean distance compression via deep random features. CoRR Brett Leroux, Luis Rademacher
2024	J	`jnl`	Expansion of random 0/1 polytopes. Random Struct. Algorithms Brett Leroux, Luis Rademacher
2024	A	`conf`	On the Nyström Approximation for Preconditioning in Kernel Machines. AISTATS Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin
2023	J	`jnl`	Improved Bounds for the Expected Number of k-Sets. Discret. Comput. Geom. Brett Leroux, Luis Rademacher
2023	J	`jnl`	On the Nystrom Approximation for Preconditioning in Kernel Machines. CoRR Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit, Luis Rademacher
2022	J	`jnl`	Algebraic k-Sets and Generally Neighborly Embeddings. Discret. Comput. Geom. Brett Leroux, Luis Rademacher
2022	J	`jnl`	Overcomplete Order-3 Tensor Decomposition, Blind Deconvolution, and Gaussian Mixture Models. SIAM J. Math. Data Sci. Haolin Chen, Luis Rademacher
2020	A*	`conf`	Efficiency of the floating body as a robust measure of dispersion. SODA Joseph Anderson, Luis Rademacher
2020	J	`jnl`	Overcomplete order-3 tensor decomposition, blind deconvolution and Gaussian mixture models. CoRR Haolin Chen, Luis Rademacher
2020	J	`jnl`	The Minimum Euclidean-Norm Point in a Convex Polytope: Wolfe's Combinatorial Algorithm is Exponential. SIAM J. Comput. Jesús A. De Loera, Jamie Haddock, Luis Rademacher
2020	J	`jnl`	The smoothed complexity of Frank-Wolfe methods via conditioning of random matrices and polytopes. CoRR Luis Rademacher, Chang Shu
2019	J	`jnl`	Algebraic k-sets and generally neighborly embeddings. CoRR Brett Leroux, Luis Rademacher
2018	J	`jnl`	Eigenvectors of Orthogonally Decomposable Functions. SIAM J. Comput. Mikhail Belkin, Luis Rademacher, James R. Voss
2018	A*	`conf`	The minimum euclidean-norm point in a convex polytope: Wolfe's combinatorial algorithm is exponential. STOC Jesús A. De Loera, Jamie Haddock, Luis Rademacher
2017	A*	`conf`	Heavy-Tailed Analogues of the Covariance Matrix for ICA. AAAI Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
2017	J	`jnl`	Heavy-Tailed Analogues of the Covariance Matrix for ICA. CoRR Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
2017	J	`jnl`	The Minimum Euclidean-Norm Point on a Convex Polytope: Wolfe's Combinatorial Algorithm is Exponential. CoRR Jesús A. De Loera, Jamie Haddock, Luis Rademacher
2016	A*	`conf`	Basis Learning as an Algorithmic Primitive. COLT Mikhail Belkin, Luis Rademacher, James R. Voss
2016	J	`jnl`	On packing and covering polyhedra in infinite dimensions. Oper. Res. Lett. Luis Rademacher, Alejandro Toriello, Juan Pablo Vielma
2016	A*	`conf`	The Hidden Convexity of Spectral Clustering. AAAI James R. Voss, Mikhail Belkin, Luis Rademacher
2015	—	`conf`	A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA. NIPS James R. Voss, Mikhail Belkin, Luis Rademacher
2015	A*	`conf`	Heavy-Tailed Independent Component Analysis. FOCS Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
2015	J	`jnl`	Heavy-tailed Independent Component Analysis. CoRR Joseph Anderson, Navin Goyal, Anupama Nandi, Luis Rademacher
2015	J	`jnl`	Optimal Recovery in Noisy ICA. CoRR James R. Voss, Mikhail Belkin, Luis Rademacher
2015	J	`jnl`	Query Complexity of Sampling and Small Geometric Partitions. Comb. Probab. Comput. Navin Goyal, Luis Rademacher, Santosh S. Vempala
2014	J	`jnl`	Expanders via Random Spanning Trees. SIAM J. Comput. Alan M. Frieze, Navin Goyal, Luis Rademacher, Santosh S. Vempala
2014	J	`jnl`	Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive. CoRR Mikhail Belkin, Luis Rademacher, James R. Voss
2014	J	`jnl`	Lower Bounds for the Average and Smoothed Number of Pareto-Optima. Theory Comput. Tobias Brunsch, Navin Goyal, Luis Rademacher, Heiko Röglin
2014	J	`jnl`	Query complexity of sampling and small geometric partitions. CoRR Navin Goyal, Luis Rademacher, Santosh S. Vempala
2014	J	`jnl`	The Hidden Convexity of Spectral Clustering. CoRR Mikhail Belkin, Luis Rademacher, James R. Voss
2014	A*	`conf`	The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. COLT Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss
2013	A*	`conf`	Blind Signal Separation in the Presence of Gaussian Noise. COLT Mikhail Belkin, Luis Rademacher, James R. Voss
2013	A*	`conf`	Efficient Learning of Simplices. COLT Joseph Anderson, Navin Goyal, Luis Rademacher
2013	—	`conf`	Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis. NIPS James R. Voss, Luis Rademacher, Mikhail Belkin
2013	J	`jnl`	The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. CoRR Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, James R. Voss
2012	J	`jnl`	Blind Signal Separation in the Presence of Gaussian Noise CoRR Mikhail Belkin, Luis Rademacher, James R. Voss
2012	J	`jnl`	Efficient learning of simplices CoRR Navin Goyal, Luis Rademacher
2012	—	`conf`	Lower Bounds for the Average and Smoothed Number of Pareto Optima. FSTTCS Navin Goyal, Luis Rademacher
2011	J	`jnl`	Lower Bounds for the Average and Smoothed Number of Pareto Optima CoRR Navin Goyal, Luis Rademacher
2010	A*	`conf`	Efficient Volume Sampling for Row/Column Subset Selection. FOCS Amit Deshpande, Luis Rademacher
2010	J	`jnl`	Efficient volume sampling for row/column subset selection CoRR Amit Deshpande, Luis Rademacher
2009	A*	`conf`	Expanders via random spanning trees. SODA Navin Goyal, Luis Rademacher, Santosh S. Vempala
2009	A*	`conf`	Learning Convex Bodies is Hard. COLT Luis Rademacher, Navin Goyal
2009	J	`jnl`	Learning convex bodies is hard CoRR Navin Goyal, Luis Rademacher
2009	J	`jnl`	Optimization of a convex program with a polynomial perturbation. Oper. Res. Lett. Ravi Kannan, Luis Rademacher
2008	J	`jnl`	Expanders via Random Spanning Trees CoRR Navin Goyal, Luis Rademacher, Santosh S. Vempala
2007	—	`conf`	Approximating the centroid is hard. SCG Luis Rademacher
2006	—	`conf`	Computing Equilibrium Prices in Exchange Economies with Tax Distortions. ICALP (1) Bruno Codenotti, Luis Rademacher, Kasturi R. Varadarajan
2006	J	`jnl`	Dispersion of Mass and the Complexity of Randomized Geometric Algorithms CoRR Luis Rademacher, Santosh S. Vempala
2006	A*	`conf`	Dispersion of Mass and the Complexity of Randomized Geometric Algorithms. FOCS Luis Rademacher, Santosh S. Vempala
2006	J	`jnl`	Dispersion of Mass and the Complexity of Randomized Geometric Algorithms. Electron. Colloquium Comput. Complex. Luis Rademacher, Santosh S. Vempala
2006	J	`jnl`	Matrix Approximation and Projective Clustering via Volume Sampling. Theory Comput. Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang
2006	A*	`conf`	Matrix approximation and projective clustering via volume sampling. SODA Amit Deshpande, Luis Rademacher, Santosh S. Vempala, Grant Wang
2004	—	`conf`	Testing Geometric Convexity. FSTTCS Luis Rademacher, Santosh S. Vempala