75 citations · 202 across the 10 of their papers we have counts for
11 papers
How did Donald Trump Surprisingly Win the 2016 United States Presidential Election? an Information-Theoretic Perspective (Clean Sensing for Big Data Analytics:Optimal Strategies,Estimation Error Bounds Tighter than the Cramér-Rao Bound)
Weiyu Xu, Lifeng Lai, Amin Khajehnejad
Donald Trump was lagging behind in nearly all opinion polls leading up to the 2016 US presidential election, but he surprisingly won the election. This raises the following importa…
Acyclicity for Groups and Vector Spaces
M. Aliabadi, H. Jolany, M. Amin Khajehnejad +2
The notion of acyclic matching property was provided by Losonczy and it was proved that torsion-free groups admit this property. In this paper, we introduce a duality of acyclic ma…
Improving the Thresholds of Sparse Recovery: An Analysis of a Two-Step Reweighted Basis Pursuit Algorithm
M. Amin Khajehnejad, Weiyu Xu, A. Salman Avestimehr +1
It is well known that minimization can be used to recover sufficiently sparse unknown signals from compressed linear measurements. In fact, exact thresholds on the sparsit…
Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing
M. Amin Khajehnejad, Juhwan Yoo, Animashree Anandkumar +1
We introduce a new class of measurement matrices for compressed sensing, using low order summaries over binary sequences of a given length. We prove recovery guarantees for three r…
Analyzing Weighted Minimization for Sparse Recovery with Nonuniform Sparse Models\footnote{The results of this paper were presented in part at the International Symposium on Information Theory, ISIT 2009}
M. Amin Khajehnejad, Weiyu Xu, A. Salman Avestimehr +1
In this paper we introduce a nonuniform sparsity model and analyze the performance of an optimized weighted minimization over that sparsity model. In particular, we focus…
Improved Sparse Recovery Thresholds with Two-Step Reweighted Minimization
M. Amin Khajehnejad, Weiyu Xu, Salman Avestimehr +1
It is well known that minimization can be used to recover sufficiently sparse unknown signals from compressed linear measurements. In fact, exact thresholds on the sparsit…