Demo Data from EDBT 2026 / reviewers in the wild / expert
Andrew Yao
dblp:95/3965
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—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 |
Graph algorithms and graph theory · 56% Combinatorics and discrete mathematics · 28% Information theory · 8% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Graph algorithms and graph theory
graph algorithms |
0.9 | 1 | 2025 | Partial and Exact Recovery of a Random Hypergraph from its Graph Projection · COLT 2025 |
Combinatorics and discrete mathematics › hypergraph
hypergraph reconstruction |
0.9 | 1 | 2025 | Partial and Exact Recovery of a Random Hypergraph from its Graph Projection · COLT 2025 |
Graph algorithms and graph theory
random graphs |
0.9 | 1 | 2025 | Partial and Exact Recovery of a Random Hypergraph from its Graph Projection · COLT 2025 |
Information theory › signal processing › compressed sensing
exact recovery |
0.3 | 1 | 2025 | Partial and Exact Recovery of a Random Hypergraph from its Graph Projection · COLT 2025 |
Algorithms and data structures › clustering
partial recovery |
0.3 | 1 | 2025 | Partial and Exact Recovery of a Random Hypergraph from its Graph Projection · COLT 2025 |
Methods — techniques the papers use, named apart from their topics
information-theoretic threshold analysis · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Partial and Exact Recovery of a Random Hypergraph from its Graph ProjectionabstractConsider a $d$-uniform random hypergraph on $n$ vertices in which hyperedges are included iid so that the average degree is $n^\delta$. The projection of a hypergraph is a graph on the same $n$ vertices where an edge connects two vertices if and only if they belong to some hyperedge. The goal is to reconstruct the hypergraph given its projection. An earlier work of Bresler, Guo, and Polyanskiy (COLT 2024) showed that exact recovery for $d=3$ is possible if and only if $\delta < 2/5$. This work completely resolves the question for all values of $d$ for both exact and partial recovery and for both cases of whether multiplicity information about each edge is available or not. In addition, we show that the reconstruction fidelity undergoes an all-or-nothing transition at a threshold. In particular, this resolves all conjectures from Bresler, Guo, and Polyanskiy (COLT 2024). Guy Bresler, Chenghao Guo, Yury Polyanskiy, Andrew Yao |
COLT | 4 |