Erixhen Sula

Biography

He received his Dipl. El.-Ing. degree from METU Ankara, Turkey, in 2013 and his MS degree from École polytechnique fédérale de Lausanne, EPFL, Switzerland, in 2016. He is currently defending his doctoral thesis at EPFL, which he started in 2016.

His research interests are in network information theory, wireless communication and coding theory.

Awards:

Dr. Bülent Kerim Altay Success Award

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Related publications (14)

Shannon Bounds on Lossy Gray-Wyner Networks

Michael Christoph Gastpar, Erixhen Sula

The Gray-Wyner network subject to a fidelity criterion is studied. Upper and lower bounds for the trade-offs between the private sum-rate and the common rate are obtained for arbitrary sources subject to mean-squared error distortion. The bounds meet exactly, leading to the computation of the rate region, when the source is jointly Gaussian. They meet partially when the sources are modeled via an additive Gaussian “channel”. The bounds are inspired from the Shannon bounds on the rate-distortion problem.

2022

The Gray-Wyner Network and Wyner's Common Information for Gaussian Sources

Michael Christoph Gastpar, Erixhen Sula

This paper presents explicit solutions for two related non-convex information extremization problems due to Gray and Wyner in the Gaussian case. The first problem is the Gray-Wyner network subject to a sum-rate constraint on the two private links. Here, our argument establishes the optimality of Gaussian codebooks and hence, a closed-form formula for the optimal rate region. The second problem is Wyner's common information and a generalization thereof, where conditional independence is generalized to a limit on the conditional mutual information. We present full explicit solutions for the scalar as well as the vector case.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Common Information Components Analysis

Michael Christoph Gastpar, Erixhen Sula

Wyner's common information is a measure that quantifies and assesses the commonality between two random variables. Based on this, we introduce a novel two-step procedure to construct features from data, referred to as Common Information Components Analysis (CICA). The first step can be interpreted as an extraction of Wyner's common information. The second step is a form of back-projection of the common information onto the original variables, leading to the extracted features. A free parameter gamma controls the complexity of the extracted features. We establish that, in the case of Gaussian statistics, CICA precisely reduces to Canonical Correlation Analysis (CCA), where the parameter gamma determines the number of CCA components that are extracted. In this sense, we establish a novel rigorous connection between information measures and CCA, and CICA is a strict generalization of the latter. It is shown that CICA has several desirable features, including a natural extension to beyond just two data sets.

MDPI2021