12 citations · 16 across the 4 of their papers we have counts for
4 papers
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians
Aleksander Mądry, Slobodan Mitrović, Ludwig Schmidt
Sparsity-based methods are widely used in machine learning, statistics, and signal processing. There is now a rich class of structured sparsity approaches that expand the modeling…
Graph-Sparse Logistic Regression
Alexander LeNail, Ludwig Schmidt, Johnathan Li +4
We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this al…
A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k
Jerry Li, Ludwig Schmidt
Learning a Gaussian mixture model (GMM) is a fundamental problem in machine learning, learning theory, and statistics. One notion of learning a GMM is proper learning: here, the go…
Sample-Optimal Density Estimation in Nearly-Linear Time
Jayadev Acharya, Ilias Diakonikolas, Jerry Li +1
We design a new, fast algorithm for agnostically learning univariate probability distributions whose densities are well approximated by piecewise polynomial functions. Let be t…