15 citations · 21 across the 6 of their papers we have counts for
7 papers
ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction
Hossein Keshavarz, Meiyappan Nagappan
In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect prediction. ApacheJIT consists of clean and bug-inducing software changes in popular Apache projects. A…
Online detection of local abrupt changes in high-dimensional Gaussian graphical models
Hossein Keshavarz, George Michailidis
The problem of identifying change points in high-dimensional Gaussian graphical models (GGMs) in an online fashion is of interest, due to new applications in biology, economics and…
A Deep Learning-Based Approach for Measuring the Domain Similarity of Persian Texts
Hossein Keshavarz, Shohreh Tabatabayi Seifi, Mohammad Izadi
In this paper, we propose a novel approach for measuring the degree of similarity between categories of two pieces of Persian text, which were published as descriptions of two sepa…
Local inversion-free estimation of spatial Gaussian processes
Hossein Keshavarz, XuanLong Nguyen, Clayton Scott
Maximizing the likelihood has been widely used for estimating the unknown covariance parameters of spatial Gaussian processes. However, evaluating and optimizing the likelihood fun…
Sequential change-point detection in high-dimensional Gaussian graphical models
Hossein Keshavarz, George Michailidis, Yves Atchade
High dimensional piecewise stationary graphical models represent a versatile class for modelling time varying networks arising in diverse application areas, including biology, econ…
On the consistency of inversion-free parameter estimation for Gaussian random fields
Hossein Keshavarz, Clayton Scott, XuanLong Nguyen
Gaussian random fields are a powerful tool for modeling environmental processes. For high dimensional samples, classical approaches for estimating the covariance parameters require…
Optimal change point detection in Gaussian processes
Hossein Keshavarz, Clayton Scott, XuanLong Nguyen
We study the problem of detecting a change in the mean of one-dimensional Gaussian process data. This problem is investigated in the setting of increasing domain (customarily emplo…