5 citations · 8 across the 4 of their papers we have counts for
7 papers
Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions
Mehmet Eren Ahsen, Robert Vogel, Gustavo Stolovitzky
Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods. Many of these strategies have been developed…
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory
Mehmet Eren Ahsen, Mathukumalli Vidyasagar
In this paper, the problem of one-bit compressed sensing (OBCS) is formulated as a problem in probably approximately correct (PAC) learning. It is shown that the Vapnik-Chervonenki…
Error Bounds for Compressed Sensing Algorithms With Group Sparsity: A Unified Approach
M. Eren Ahsen, M. Vidyasagar
In compressed sensing, in order to recover a sparse or nearly sparse vector from possibly noisy measurements, the most popular approach is -norm minimization. Upper bounds…
Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect
Mehmet Eren Ahsen, Niharika Challapalli, Mathukumalli Vidyasagar
In this paper we introduce a new optimization formulation for sparse regression and compressed sensing, called CLOT (Combined L-One and Two), wherein the regularizer is a convex co…
Near-Ideal Behavior of Compressed Sensing Algorithms
Mehmet Eren Ahsen, Mathukumalli Vidyasagar
In a recent paper, it is shown that the LASSO algorithm exhibits "near-ideal behavior," in the following sense: Suppose where satisfies the restricted isometry pro…
Reverse Engineering Gene Interaction Networks Using the Phi-Mixing Coefficient
Nitin Kumar Singh, M. Eren Ahsen, Shiva Mankala +3
Constructing gene interaction networks (GINs) from high-throughput gene expression data is an important and challenging problem in systems biology. Existing algorithms produce netw…
Mixing Coefficients Between Discrete and Real Random Variables: Computation and Properties
Mehmet Eren Ahsen, Mathukumalli Vidyasagar
In this paper we study the problem of estimating the alpha-, beta- and phi-mixing coefficients between two random variables, that can either assume values in a finite set or the se…