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cs.DS2017★ 1 cited
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…
cs.DS2015★ 12 cited
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…
cs.DS2015★ 2 cited
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…