5 citations · 11 across the 10 of their papers we have counts for
13 papers
Efficient learning of differential network in multi-source non-paranormal graphical models
Mojtaba Nikahd, Seyed Abolfazl Motahari
This paper addresses learning of sparse structural changes or differential network between two classes of non-paranormal graphical models. We assume a multi-source and heterogeneou…
Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
Amir Hossein Saberi, Amir Najafi, Seyed Abolfazl Motahari +1
In this paper, we find a sample complexity bound for learning a simplex from noisy samples. Assume a dataset of size is given which includes i.i.d. samples drawn from a uniform…
Isoform Function Prediction Using a Deep Neural Network
Sara Ghazanfari, Ali Rasteh, Seyed Abolfazl Motahari +1
Isoforms are mRNAs produced from the same gene site in the phenomenon called Alternative Splicing. Studies have shown that more than 95% of human multi-exon genes have undergone al…
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…
Learning of Tree-Structured Gaussian Graphical Models on Distributed Data under Communication Constraints
Mostafa Tavassolipour, Seyed Abolfazl Motahari, Mohammad-Taghi Manzuri Shalmani
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where eac…
On the Identifiability of Finite Mixtures of Finite Product Measures
Behrooz Tahmasebi, Seyed Abolfazl Motahari, Mohammad Ali Maddah-Ali
The problem of identifiability of finite mixtures of finite product measures is studied. A mixture model with mixture components and observed variables is considered, where…
Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks
Farzad Abdolhosseini, Behrooz Azarkhalili, Abbas Maazallahi +4
Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but…
Learning of Gaussian Processes in Distributed and Communication Limited Systems
Mostafa Tavassolipour, Seyed Abolfazl Motahari, Mohammad-Taghi Manzuri Shalmani
It is of fundamental importance to find algorithms obtaining optimal performance for learning of statistical models in distributed and communication limited systems. Aiming at char…