Demo Data from EDBT 2026 / reviewers in the wild / expert
Karim Ezzeddine
dblp:393/0164
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0009-4796-8434ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Coding theory · 93% Information theory · 7% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › source coding
lossy source coding |
1.0 | 1 | 2026 | A Framework for Lossy Compression of Heavy-Tailed Sources · IEEE Trans. Commun. 2026 |
Coding theory › source coding
quantization |
1.0 | 1 | 2026 | A Framework for Lossy Compression of Heavy-Tailed Sources · IEEE Trans. Commun. 2026 |
Coding theory › source coding
rate-distortion theory |
1.0 | 1 | 2026 | A Framework for Lossy Compression of Heavy-Tailed Sources · IEEE Trans. Commun. 2026 |
Coding theory
source coding |
1.0 | 1 | 2026 | A Framework for Lossy Compression of Heavy-Tailed Sources · IEEE Trans. Commun. 2026 |
Methods — techniques the papers use, named apart from their topics
rate-distortion analysis · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | A Framework for Lossy Compression of Heavy-Tailed SourcesabstractWe study the rate-distortion problem for both scalar and vector memoryless heavy-tailed α-stable sources (0 < α < 2). Using a recently defined notion of “strength” as a power measure, we derive the rate-distortion function for α-stable sources subject to a constraint on the strength of the error and show it to be logarithmic in the strength-to-distortion ratio. We show how our framework paves the way for finding optimal quantizers for α- stable sources and other general heavy-tailed ones. In addition, we study high-rate scalar quantizers and show that uniform ones are asymptotically optimal under the error-strength distortion measure. We compare uniform Gaussian and Cauchy quantizers and show that more representation points for the Cauchy source are required to guarantee the same quantization quality. Our findings generalize the well-known results of rate-distortion and quantization of Gaussian sources (α = 2) under a quadratic distortion measure. Karim Ezzeddine, Jihad Fahs, Ibrahim C. Abou-Faycal |
IEEE Trans. Commun. | 1 |
| 2025 | On the Lossy Compression of Stable SourcesabstractWe propose a framework for the rate-distortion analysis that extends the Gaussian MSE setup for stable sources. Specifically, we study the rate-distortion function of$\alpha$-stable sources when the distortion constraint is specified using the recently defined notion of strength of the error variable. We also “generalize” the notion of$d$-tilted information under the new setup and use it to determine second-order terms for both fixed and variable-length codes. Karim Ezzeddine, Jihad Fahs, Ibrahim C. Abou-Faycal |
ISIT | 1 |