Demo Data from EDBT 2026 / reviewers in the wild / expert
Jiseong Park
dblp:126/1765
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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 |
Algorithms and data structures · 50% Combinatorics and discrete mathematics · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithms and data structures › randomized algorithms › sampling
resampling algorithms |
0.1 | 1 | 2012 | Fast Resampling Weighted v-Statistics · NIPS 2012 |
Methods — techniques the papers use, named apart from their topics
resampling · 0.1orbit enumeration · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Fast Resampling Weighted v-StatisticsabstractIn this paper, a novel, computationally fast, and alternative algorithm for com- puting weighted v-statistics in resampling both univariate and multivariate data is proposed. To avoid any real resampling, we have linked this problem with finite group action and converted it into a problem of orbit enumeration. For further computational cost reduction, an efficient method is developed to list all orbits by their symmetry order and calculate all index function orbit sums and data function orbit sums recursively. The computational complexity analysis shows reduction in the computational cost from n! or nn level to low-order polynomial level. Chunxiao Zhou, Jiseong Park, Yun Fu 0001 |
NIPS | 2 |