10 papers
Distributed Sparse Feature Selection in Communication-Restricted Networks
Hanie Barghi, Amir Najafi, Seyed Abolfazl Motahari
This paper aims to propose and theoretically analyze a new distributed scheme for sparse linear regression and feature selection. The primary goal is to learn the few causal featur…
Regularizing Recurrent Neural Networks via Sequence Mixup
Armin Karamzade, Amir Najafi, Seyed Abolfazl Motahari
In this paper, we extend a class of celebrated regularization techniques originally proposed for feed-forward neural networks, namely Input Mixup (Zhang et al., 2017) and Manifold…
Private Shotgun DNA Sequencing: A Structured Approach
Ali Gholami, Mohammad Ali Maddah-Ali, Seyed Abolfazl Motahari
DNA sequencing has faced a huge demand since it was first introduced as a service to the public. This service is often offloaded to the sequencing companies who will have access to…
Structure Learning of Sparse GGMs over Multiple Access Networks
Mostafa Tavassolipour, Armin Karamzade, Reza Mirzaeifard +2
A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple local machines. The loca…
Information Theoretic Bounds on Optimal Worst-case Error in Binary Mixture Identification
Khashayar Gatmiry, Seyed Abolfazl Motahari
Identification of latent binary sequences from a pool of noisy observations has a wide range of applications in both statistical learning and population genetics. Each observed seq…
Private Shotgun DNA Sequencing
Ali Gholami, Mohammad Ali Maddah-Ali, Seyed Abolfazl Motahari
Current techniques in sequencing a genome allow a service provider (e.g. a sequencing company) to have full access to the genome information, and thus the privacy of individuals re…