28 citations · 48 across the 8 of their papers we have counts for
5 papers · 1 filter
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs
Sheng Xu, Zhou Fan, Sahand Negahban
We study estimation of a gradient-sparse parameter vector , having strong gradient-sparsity on an underlying g…
Minimax Estimation of Bandable Precision Matrices
Addison Hu, Sahand Negahban
The inverse covariance matrix provides considerable insight for understanding statistical models in the multivariate setting. In particular, when the distribution over variables is…
Scalable Greedy Feature Selection via Weak Submodularity
Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis +2
Greedy algorithms are widely used for problems in machine learning such as feature selection and set function optimization. Unfortunately, for large datasets, the running time of e…
On Approximation Guarantees for Greedy Low Rank Optimization
Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis +1
We provide new approximation guarantees for greedy low rank matrix estimation under standard assumptions of restricted strong convexity and smoothness. Our novel analysis also unco…
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions
Alekh Agarwal, Sahand Negahban, Martin J. Wainwright
We develop and analyze stochastic optimization algorithms for problems in which the expected loss is strongly convex, and the optimum is (approximately) sparse. Previous approaches…