Testing Geometric Convexity. - Luis Rademacher; Santosh S. Vempala (FSTTCS, 2004)

Computing Equilibrium Prices in Exchange Economies with Tax Distortions. - Bruno Codenotti; Luis Rademacher; Kasturi R. Varadarajan (ICALP (1), 2006)

Dispersion of Mass and the Complexity of Randomized Geometric Algorithms. - Luis Rademacher; Santosh S. Vempala (Electron. Colloquium Comput. Complex., 2006)

Matrix Approximation and Projective Clustering via Volume Sampling. - Amit Deshpande 001; Luis Rademacher; Santosh S. Vempala; Grant Wang (Theory Comput., 2006)

Approximating the centroid is hard. - Luis Rademacher (SCG, 2007)

Learning Convex Bodies is Hard. - Luis Rademacher; Navin Goyal (COLT, 2009)

Expanders via random spanning trees. - Navin Goyal; Luis Rademacher; Santosh S. Vempala (SODA, 2009)

Optimization of a convex program with a polynomial perturbation. - Ravi Kannan; Luis Rademacher (Oper. Res. Lett., 2009)

Efficient Volume Sampling for Row/Column Subset Selection. - Amit Deshpande 001; Luis Rademacher (FOCS, 2010)

Lower Bounds for the Average and Smoothed Number of Pareto Optima. - Navin Goyal; Luis Rademacher (FSTTCS, 2012)

Efficient learning of simplices - Navin Goyal; Luis Rademacher (CoRR, 2012)

Efficient Learning of Simplices. - Joseph Anderson; Navin Goyal; Luis Rademacher (COLT, 2013)

Blind Signal Separation in the Presence of Gaussian Noise. - Mikhail Belkin; Luis Rademacher; James R. Voss (COLT, 2013)

Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis. - James R. Voss; Luis Rademacher; Mikhail Belkin (NIPS, 2013)

The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures. - Joseph Anderson; Mikhail Belkin; Navin Goyal; Luis Rademacher; James R. Voss (COLT, 2014)

Learning a Hidden Basis Through Imperfect Measurements: An Algorithmic Primitive. - Mikhail Belkin; Luis Rademacher; James R. Voss (CoRR, 2014)

Expanders via Random Spanning Trees. - Alan M. Frieze; Navin Goyal; Luis Rademacher; Santosh S. Vempala (SIAM J. Comput., 2014)

Lower Bounds for the Average and Smoothed Number of Pareto-Optima. - Tobias Brunsch; Navin Goyal; Luis Rademacher; Heiko Röglin (Theory Comput., 2014)

Heavy-Tailed Independent Component Analysis. - Joseph Anderson; Navin Goyal; Anupama Nandi; Luis Rademacher (FOCS, 2015)

A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA. - James R. Voss; Mikhail Belkin; Luis Rademacher (NIPS, 2015)

Optimal Recovery in Noisy ICA. - James R. Voss; Mikhail Belkin; Luis Rademacher (CoRR, 2015)

Query Complexity of Sampling and Small Geometric Partitions. - Navin Goyal; Luis Rademacher; Santosh S. Vempala (Comb. Probab. Comput., 2015)

The Hidden Convexity of Spectral Clustering. - James R. Voss; Mikhail Belkin; Luis Rademacher (AAAI, 2016)

Basis Learning as an Algorithmic Primitive. - Mikhail Belkin; Luis Rademacher; James R. Voss (COLT, 2016)

On packing and covering polyhedra in infinite dimensions. - Luis Rademacher; Alejandro Toriello; Juan Pablo Vielma (Oper. Res. Lett., 2016)

Heavy-Tailed Analogues of the Covariance Matrix for ICA. - Joseph Anderson; Navin Goyal; Anupama Nandi; Luis Rademacher (AAAI, 2017)

Eigenvectors of Orthogonally Decomposable Functions. - Mikhail Belkin; Luis Rademacher; James R. Voss (SIAM J. Comput., 2018)

Efficiency of the floating body as a robust measure of dispersion. - Joseph Anderson; Luis Rademacher (SODA, 2020)

The smoothed complexity of Frank-Wolfe methods via conditioning of random matrices and polytopes. - Luis Rademacher; Chang Shu (CoRR, 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)

Algebraic k-Sets and Generally Neighborly Embeddings. - Brett Leroux; Luis Rademacher (Discret. Comput. Geom., 2022)

Overcomplete Order-3 Tensor Decomposition, Blind Deconvolution, and Gaussian Mixture Models. - Haolin Chen; Luis Rademacher (SIAM J. Math. Data Sci., 2022)

Improved Bounds for the Expected Number of k-Sets. - Brett Leroux; Luis Rademacher (Discret. Comput. Geom., 2023)

On the Nyström Approximation for Preconditioning in Kernel Machines. - Amirhesam Abedsoltan; Parthe Pandit; Luis Rademacher; Mikhail Belkin (AISTATS, 2024)

Euclidean distance compression via deep random features. - Brett Leroux; Luis Rademacher (NeurIPS, 2024)

Expansion of random 0/1 polytopes. - Brett Leroux; Luis Rademacher (Random Struct. Algorithms, 2024)