4 papers
A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation
Elif Vural, Huseyin Karaca
Domain adaptation seeks to leverage the abundant label information in a source domain to improve classification performance in a target domain with limited labels. While the field…
Graph Signal Inference by Learning Narrowband Spectral Kernels
Osman Furkan Kar, Gülce Turhan, Elif Vural
While a common assumption in graph signal analysis is the smoothness of the signals or the band-limitedness of their spectrum, in many instances the spectrum of real graph data may…
Downlink Channel Covariance Matrix Estimation via Representation Learning with Graph Regularization
Melih Can Zerin, Elif Vural, Ali Özgür Yılmaz
In this paper, we propose an algorithm for downlink (DL) channel covariance matrix (CCM) estimation for frequency division duplexing (FDD) massive multiple-input multiple-output (M…
Locally Stationary Graph Processes
Abdullah Canbolat, Elif Vural
Stationary graph process models are commonly used in the analysis and inference of data sets collected on irregular network topologies. While most of the existing methods represent…