EDBT 2026 Demo / reviewers in the wild / expert
Yurii Lyubarskii
dblp:49/8860
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2010
0000-0002-5487-2969ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 first-author
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 |
Information theory · 50% Coding theory · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › error-correcting codes › error probability analysis
error coefficient optimization |
0.1 | 1 | 2010 | Uncertainty principles and vector quantization · IEEE Trans. Inf. Theory 2010 |
Information theory › signal processing › signal representation
frame theory |
0.1 | 1 | 2010 | Uncertainty principles and vector quantization · IEEE Trans. Inf. Theory 2010 |
Information theory › signal processing › time-frequency analysis
uncertainty principle |
0.1 | 1 | 2010 | Uncertainty principles and vector quantization · IEEE Trans. Inf. Theory 2010 |
Coding theory › source coding › quantization
vector quantization |
0.1 | 1 | 2010 | Uncertainty principles and vector quantization · IEEE Trans. Inf. Theory 2010 |
Methods — techniques the papers use, named apart from their topics
frame representation · 0.1dynamic range analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Uncertainty principles and vector quantizationabstractGiven a frame in Cnwhich satisfies a form of the uncertainty principle (as introduced by Candes and Tao), it is shown how to quickly convert the frame representation of every vector into a more robust Kashin's representation whose coefficients all have the smallest possible dynamic rangeO(1/√(n). The information tends to spread evenly among these coefficients. As a consequence, Kashin's representations have a great power for reduction of errors in their coefficients, including coefficient losses and distortions. Yurii Lyubarskii, Roman Vershynin |
IEEE Trans. Inf. Theory | 1 |