Demo Data from EDBT 2026 / reviewers in the wild / expert
Giulio Zani
dblp:382/3607
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 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 · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › source coding
entropy coding |
0.8 | 1 | 2024 | Entropy Coding of Unordered Data Structures · ICLR 2024 |
Coding theory
source coding |
0.8 | 1 | 2024 | Entropy Coding of Unordered Data Structures · ICLR 2024 |
Methods — techniques the papers use, named apart from their topics
bits-back coding · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Entropy Coding of Unordered Data StructuresabstractWe present shuffle coding, a general method for optimal compression of sequences of unordered objects using bits-back coding. Data structures that can be compressed using shuffle coding include multisets, graphs, hypergraphs, and others. We release an implementation that can easily be adapted to different data types and statistical models, and demonstrate that our implementation achieves state-of-the-art compression rates on a range of graph datasets including molecular data. Julius Kunze, Daniel Severo 0001, Giulio Zani, Jan-Willem van de Meent, James Townsend |
ICLR | 3 |