13 citations · 20 across the 6 of their papers we have counts for
10 papers
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs
Jonathan A. Kelner, Frederic Koehler, Raghu Meka +1
Sparse linear regression with ill-conditioned Gaussian random designs is widely believed to exhibit a statistical/computational gap, but there is surprisingly little formal evidenc…
Robust Generalized Method of Moments: A Finite Sample Viewpoint
Dhruv Rohatgi, Vasilis Syrgkanis
For many inference problems in statistics and econometrics, the unknown parameter is identified by a set of moment conditions. A generic method of solving moment conditions is the…
On the Power of Preconditioning in Sparse Linear Regression
Jonathan Kelner, Frederic Koehler, Raghu Meka +1
Sparse linear regression is a fundamental problem in high-dimensional statistics, but strikingly little is known about how to efficiently solve it without restrictive conditions on…
Truncated Linear Regression in High Dimensions
Constantinos Daskalakis, Dhruv Rohatgi, Manolis Zampetakis
As in standard linear regression, in truncated linear regression, we are given access to observations whose dependent variable equals $y_i= A_i^{\rm T} \cdot x^* + η…
Constant-Expansion Suffices for Compressed Sensing with Generative Priors
Constantinos Daskalakis, Dhruv Rohatgi, Manolis Zampetakis
Generative neural networks have been empirically found very promising in providing effective structural priors for compressed sensing, since they can be trained to span low-dimensi…
Regarding two conjectures on clique and biclique partitions
Dhruv Rohatgi, John C. Urschel, Jake Wellens
For a graph , let denote the minimum number of cliques of needed to cover the edges of exactly once. Similarly, let denote the minimum number of bicliq…